[ { "$schema": "../../schemas/tool.schema.json", "slug": "13c-spacem", "name": "13C-SpaceM analysis code", "summary": "Analyze spatial carbon-13 labeling of fatty acids in single cells and tissue imaging experiments.", "description": "The 13C-SpaceM repository provides scripts and notebooks supporting the published spatial isotope-tracing method. It performs isotope correction and single-cell or pixel-level analysis using prepared imaging and registration outputs. It is research code, not a self-contained replacement for the SpaceM processing pipeline.", "links": { "primary": "https://github.com/Buglakova/13C-SpaceM", "repository": "https://github.com/Buglakova/13C-SpaceM", "documentation": "https://github.com/Buglakova/13C-SpaceM" }, "platforms": [ "imaging_ms", "maldi_ms" ], "interfaces": [ "command_line", "notebook" ], "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "isotope_tracing_flux", "capabilities": [ "isotopologue_correction" ] }, "access": { "model": "source_available", "notes": "Public code; no explicit repository license verified. The README describes a separate closed-source SpaceM registration step and supplies its outputs for reproduction." }, "data": { "input_types": [ "imaging_data", "isotope_tracing_data" ], "output_types": [ "isotope_tracing_data", "report" ], "other_input_formats": [ "imzML ion images and prepared SpaceM registration/segmentation outputs" ], "other_output_formats": [ "AnnData objects and figure-generation notebooks" ] }, "common_uses": [ "Reproduce or adapt spatial fatty-acid isotope-tracing analysis." ], "scope": { "designed_for": [ "The published single-cell and tissue isotope-tracing workflow." ], "not_designed_for": [ "A turnkey raw-image registration package." ], "requirements": [ "Prepared SpaceM outputs for single-cell analysis and the provided Conda environment." ], "considerations": [ "The authors flag dependency-version compatibility issues." ] }, "publications": [ { "doi": "10.1038/s42255-024-01118-4", "type": "primary", "citation": "Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer. Nature Metabolism (2024)." } ], "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Discovered through the Enveda review intake; official repository and primary publication metadata checked on 2026-10-02. Editorial review only; software and performance not execution-tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "aerith", "name": "Aerith", "summary": "An R package for reading, annotating, scoring, and visualizing isotopic mass spectra in stable-isotope-probing workflows for metabolites and peptides.", "links": { "primary": "https://github.com/thepanlab/Aerith", "repository": "https://github.com/thepanlab/Aerith", "documentation": "https://github.com/thepanlab/Aerith#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "isotope_tracing_flux", "secondary": [ "spectral_analysis", "visualization_reporting" ], "capabilities": [ "in_silico_spectra", "spectral_data_io" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "stable_isotope_tracing" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 repository. External raw-file converters and peptide-analysis tools used in examples are separate dependencies rather than mandatory parts of every metabolite simulation." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R", "C++" ] }, "data": { "input_types": [ "spectra", "isotope_tracing_data" ], "output_types": [ "isotope_tracing_data", "report" ], "other_input_formats": [ "Molecular formulas and isotope abundances; FT1/FT2 scan files by module" ], "other_output_formats": [ "R isotope-pattern or scan objects and spectrum/TIC plots" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-04-30", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "Aerith reads and visualizes MS scans and models isotope envelopes under stable-isotope enrichment. Its metabolomics-facing functions calculate expected isotope patterns from molecular formulas; other modules support peptide isotope-pattern and spectrum inspection.", "scope": { "designed_for": [ "An R package for reading, annotating, scoring, and visualizing isotopic mass spectra in stable-isotope-probing workflows for metabolites and peptides." ], "not_designed_for": [ "Metabolic flux inference, automatic correction of every isotope-tracing dataset or metabolite identification from an envelope alone." ], "requirements": [ "R with the documented compiled dependencies; supply molecular formulas and isotope abundances for simulation, or supported scan files for experimental visualization." ], "considerations": [ "Distinguish molecular-formula simulation from the peptide-oriented demonstration workflow. The chosen isotope enrichment and elemental composition determine the theoretical envelope." ] }, "common_uses": [ "An R package for reading, annotating, scoring, and visualizing isotopic mass spectra in stable-isotope-probing workflows for metabolites and peptides." ], "publications": [ { "doi": "10.1021/acs.analchem.5c03207", "type": "primary", "citation": "Aerith: Visualization and Annotation of Isotopic Enrichment Patterns of Peptides and Metabolites with Stable Isotope Labeling from Proteomics and Metabolomics. Analytical Chemistry (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "airdpro", "name": "AirdPro", "summary": "AirdPro converts mass-spectrometry vendor data into the Aird storage format through a graphical batch-conversion workflow.", "links": { "primary": "https://github.com/CSi-Studio/AirdPro", "repository": "https://github.com/CSi-Studio/AirdPro", "documentation": "https://github.com/CSi-Studio/AirdPro#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "data_conversion", "secondary": [], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "MulanPSL-2.0", "notes": "MulanPSL-2.0 repository. Vendor readers, SDKs and associated software have their own compatibility and licensing requirements." }, "technical": { "operating_systems": [ "windows" ], "programming_languages": [ "C#" ] }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "spectra" ], "other_output_formats": [ "Paired Aird spectral binary and JSON index files" ], "other_input_formats": [ "Vendor MS files through supported ProteoWizard readers" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-12-01", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "AirdPro converts mass-spectrometry vendor data into the Aird storage format through a graphical batch-conversion workflow. Aird separates compressed spectral arrays from a JSON index for access through the companion SDKs.", "scope": { "designed_for": [ "AirdPro converts mass-spectrometry vendor data into the Aird storage format through a graphical batch-conversion workflow." ], "not_designed_for": [ "Feature detection, metabolite identification or a guarantee that every vendor file is readable on every operating system." ], "requirements": [ "A compatible AirdPro installation and vendor readers supplied through ProteoWizard; preserve each Aird binary together with its matching JSON index." ], "considerations": [ "Select and document compression and precision settings. Precision-controlled lossy conversion requires assessment against the downstream scientific use." ] }, "common_uses": [ "AirdPro converts mass-spectrometry vendor data into the Aird storage format through a graphical batch-conversion workflow." ], "publications": [ { "doi": "10.1186/s12859-021-04490-0", "type": "primary", "citation": "Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time. BMC Bioinformatics (2022).", "note": "Aird format and associated conversion implementation; reader compatibility is installation-dependent." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "alister", "name": "ALISTER", "summary": "ALISTER provides literature-based information about pre-analytical metabolite and lipid stability in EDTA plasma and serum.", "links": { "primary": "https://github.com/Fraunhofer-ITMP/alister", "repository": "https://github.com/Fraunhofer-ITMP/alister", "web_app": "https://itmp.shinyapps.io/alister/", "documentation": "https://github.com/Fraunhofer-ITMP/alister#readme" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "secondary": [ "quality_control" ], "capabilities": [ "metadata_contextualization" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling", "lipidomics" ], "interfaces": [ "web_app" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "browser" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "sample_metadata" ], "output_types": [ "report" ], "other_input_formats": [ "Interactive matrix, temperature, processing-delay and analyte selections" ], "other_output_formats": [ "Stability-query CSV tables, references and sampling-method PDF/plots" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-05-15", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "ALISTER provides literature-based information about pre-analytical metabolite and lipid stability in EDTA plasma and serum. Its Shiny interface supports sample-condition queries, analyte searches and prospective sampling-method exploration.", "scope": { "designed_for": [ "ALISTER provides literature-based information about pre-analytical metabolite and lipid stability in EDTA plasma and serum." ], "not_designed_for": [ "Measuring actual sample integrity or generalizing supported conditions to every specimen matrix and handling procedure." ], "requirements": [ "A browser for the hosted application or a local R/Shiny installation; supply sample matrix, handling delays, temperatures and analytes or classes of interest." ], "considerations": [ "Evidence coverage and the application's generalization of input conditions constrain interpretation. Review the underlying references and chosen change thresholds for each query." ] }, "common_uses": [ "ALISTER provides literature-based information about pre-analytical metabolite and lipid stability in EDTA plasma and serum." ], "publications": [ { "doi": "10.1016/j.cca.2024.117858", "type": "primary", "citation": "ALISTER – Application for lipid stability evaluation and research. Clinica Chimica Acta (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "amanida", "name": "Amanida", "summary": "An R package for quantitative and qualitative meta-analysis of metabolomics studies when only significance, effect-size, identifier, or direction information is available.", "links": { "primary": "https://github.com/mariallr/amanida", "repository": "https://github.com/mariallr/amanida", "documentation": "https://github.com/mariallr/amanida#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "secondary": [], "capabilities": [ "univariate_statistics" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "statistical_results", "compound_list" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "CSV, TXT or Excel tables of study-level results and sample sizes" ], "other_output_formats": [ "R statistical/vote-count tables, volcano plots and rendered reports" ] }, "common_uses": [ "Combine metabolomics evidence across studies when variance data are unavailable." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-01-26", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "Amanida combines study-level metabolomics results when individual measurements or variance estimates are unavailable. It performs significance/fold-change meta-analysis and qualitative trend vote-counting, with plots and reports for comparing results across studies.", "scope": { "designed_for": [ "An R package for quantitative and qualitative meta-analysis of metabolomics studies when only significance, effect-size, identifier, or direction information is available." ], "not_designed_for": [ "Reprocessing raw MS data or removing heterogeneity and publication bias merely by pooling reported p-values." ], "requirements": [ "R plus harmonized metabolite identifiers, study references and either p-values/fold changes/sample sizes or qualitative trend labels; input column order must follow the selected mode." ], "considerations": [ "Align metabolite identities and fold-change direction before combination. Study dependence, selective reporting and incompatible designs affect interpretation; optional identifier harmonization can query PubChem." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btab591", "type": "primary", "citation": "Amanida: an R package for meta-analysis of metabolomics non-integral data. Bioinformatics (2021)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "arcms", "name": "arcMS", "summary": "arcMS converts Waters UNIFI MS-E and HDMS-E data into tabular Parquet or HDF5 storage for analysis in R or Python.", "links": { "primary": "https://github.com/leesulab/arcMS", "repository": "https://github.com/leesulab/arcMS", "documentation": "https://leesulab.github.io/arcMS/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_conversion", "secondary": [ "data_management_sharing" ], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "spectra" ], "other_output_formats": [ "Parquet plus JSON metadata, or HDF5 spectral/metadata datasets" ], "other_input_formats": [ "UNIFI API MS-E/HDMS-E data or supported mzML files" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-06-07", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "arcMS converts Waters UNIFI MS-E and HDMS-E data into tabular Parquet or HDF5 storage for analysis in R or Python. It also documents mzML-to-Parquet conversion, metadata retrieval and access to mobility/CCS information when available.", "scope": { "designed_for": [ "arcMS converts Waters UNIFI MS-E and HDMS-E data into tabular Parquet or HDF5 storage for analysis in R or Python." ], "not_designed_for": [ "Automatic feature detection, spectral deconvolution or molecular identification from the converted data." ], "requirements": [ "R and documented dependencies; UNIFI conversion requires configured API access and registered connection credentials, while HDF5 use requires its supporting packages." ], "considerations": [ "Keep acquisition metadata with exported arrays and distinguish measured mobility/CCS fields from predicted values. The separate companion visualization app has its own setup." ] }, "common_uses": [ "arcMS converts Waters UNIFI MS-E and HDMS-E data into tabular Parquet or HDF5 storage for analysis in R or Python." ], "publications": [ { "doi": "10.1093/bioadv/vbae160", "type": "primary", "citation": "arcMS: transformation of multi-dimensional high-resolution mass spectrometry data to columnar format for compact storage and fast access. Bioinformatics Advances (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "asari", "name": "asari", "summary": "Process high-resolution mass-spectrometry data into aligned feature tables.", "description": "Asari uses mass alignment and a composite representation of chromatographic signals to extract features across samples. Its default workflow focuses on LC-MS feature processing.", "links": { "primary": "https://github.com/shuzhao-li-lab/asari", "documentation": "https://github.com/shuzhao-li-lab/asari", "repository": "https://github.com/shuzhao-li-lab/asari" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "peak_detection", "retention_time_alignment" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "source_available", "notes": "Source and installation instructions are public; consult the repository license for the version used." }, "data": { "input_types": [ "spectra" ], "output_types": [ "feature_table" ], "input_formats": [ "mzml" ], "output_formats": [ "csv_tsv", "json" ] }, "publications": [ { "citation": "Li S, Siddiqa A, Thapa M, et al. Trackable and scalable LC-MS metabolomics data processing using asari. Nat Commun 14, 4113 (2023).", "type": "primary", "doi": "10.1038/s41467-023-39889-1" } ], "common_uses": [ "Build a feature table from centroided LC-MS files.", "Prepare data for downstream quality control and statistics." ], "scope": { "designed_for": [ "Build a feature table from centroided LC-MS files." ], "not_designed_for": [ "Assigning confirmed chemical structures from feature detection alone." ], "requirements": [ "Centroided mzML data and a compatible Python environment." ], "considerations": [ "Check mass tolerances, chromatography and acquisition mode against the workflow; default processing and specialized workflows differ." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "xcms", "relationship": "complementary" }, { "slug": "mzmine", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "autoccs", "name": "AutoCCS", "summary": "An open-source Python tool for calculating and calibrating experimental collision cross sections from ion-mobility mass-spectrometry data across drift-tube and traveling-wave workflows.", "links": { "primary": "https://github.com/PNNL-Comp-Mass-Spec/AutoCCS", "repository": "https://github.com/PNNL-Comp-Mass-Spec/AutoCCS", "documentation": "https://github.com/PNNL-Comp-Mass-Spec/AutoCCS#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [ "annotation_identification" ], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "BSD-2-Clause", "notes": "BSD-2-Clause software with worked examples. Users must supply or generate instrument-compatible feature and calibration inputs." }, "technical": { "operating_systems": [ "linux", "macos", "windows" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Feature/target CSV, XML configuration and acquisition/calibrant metadata" ], "other_output_formats": [ "Experimental CCS result tables and calibration outputs" ] }, "publications": [ { "doi": "10.1093/bioinformatics/btab429", "type": "primary", "citation": "AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry–mass spectrometry. Bioinformatics (2021)." } ], "common_uses": [ "Calculate CCS values from stepped-field or single-field drift-tube IMS-MS measurements.", "Calibrate traveling-wave or SLIM ion-mobility measurements using reference compounds." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-12-13", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Processing ion-mobility measurements into calibrated collision cross sections." ], "not_designed_for": [ "CCS prediction directly from a molecular structure or compound identification based on CCS alone." ], "requirements": [ "Python environment, feature CSVs, an XML instrument configuration and acquisition metadata; single-field/TWIMS modes also require suitable calibrants and sample metadata." ], "considerations": [ "Calibration model, reference CCS values and acquisition conditions affect reported values. Check method applicability before comparing CCS across platforms." ] }, "acquisition": { "ms_levels": [ "ms1" ], "strategies": [ "unspecified" ], "ion_mobility": "required" }, "description": "AutoCCS calculates experimental collision cross sections from ion-mobility measurements using stepped-field, single-field and traveling-wave workflows. It applies mode-specific calibration functions to prepared feature and instrument metadata." }, { "$schema": "../../schemas/tool.schema.json", "slug": "autonoms", "name": "AutonoMS", "summary": "Automate ion-mobility metabolomic fingerprinting on a configured RapidFire system.", "description": "AutonoMS coordinates sample acquisition, raw-data handling, CCS calibration and Skyline analysis for the documented Agilent RapidFire–6560 workflow.", "links": { "primary": "https://autonoms.readthedocs.io/en/latest/", "repository": "https://github.com/gkreder/autonoms", "documentation": "https://autonoms.readthedocs.io/en/latest/" }, "platforms": [ "direct_infusion_ms" ], "interfaces": [ "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "data_acquisition", "capabilities": [ "peak_detection" ] }, "access": { "model": "source_available", "notes": "Source and workflow documentation are public; an explicit repository software license was not identified. Instrument/vendor software is required separately. Not execution-tested." }, "data": { "input_types": [ "sample_metadata", "raw_instrument_data", "compound_list" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "Experiment XLSX and configuration TOML", "Target transitions, tune-ion standards and Skyline files" ], "other_output_formats": [ "Acquisition files and Skyline tabular signal/CCS reports" ] }, "common_uses": [ "Automate ion-mobility metabolomic fingerprinting on a configured RapidFire system." ], "scope": { "designed_for": [ "AutonoMS coordinates sample acquisition, raw-data handling, CCS calibration and Skyline analysis for the documented Agilent RapidFire–6560 workflow." ], "not_designed_for": [ "A general instrument-control solution for arbitrary vendors or an autonomous AI scientist." ], "requirements": [ "Configured Agilent RapidFire–6560 hardware, MassHunter, RapidFire UI, PNNL PreProcessor, MSConvert and Skyline.", "A tune-ion injection with known CCS values at the start of each sequence; matching transition and ion-mobility library files.", "Python 3.8 as specified in package metadata, with Prefect and DEIMoS dependencies." ], "considerations": [ "The supplied implementation depends on specific vendor GUI layout and paths; review configuration before instrument use.", "Target matching and fingerprints do not establish unambiguous metabolite identities." ] }, "publications": [ { "doi": "10.1021/jasms.3c00396", "type": "primary", "citation": "AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting. Journal of the American Society for Mass Spectrometry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "related_tools": [ { "slug": "skyline", "relationship": "dependency", "note": "Uses Skyline documents, transition lists and report templates for data analysis." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "bago", "name": "BAGO", "aliases": [ "Bayesian Optimization for LC Gradient Optimization" ], "summary": "BAGO is an open-source bioinformatics tool designed for the systematic optimization of LC gradient using the Bayesian optimization framework. It is particularly useful for optimizing LC gradients in LC-MS-based metabolomics, lipidomics, and exposomics.", "links": { "primary": "https://github.com/HuanLab/bago", "documentation": "https://bago.readthedocs.io/en/latest/", "repository": "https://github.com/Waddlessss/bago", "homepage": "https://github.com/HuanLab/bago" }, "resource_types": [ "software_application" ], "functions": { "primary": "data_acquisition", "secondary": [], "capabilities": [] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "desktop_gui", "python_package" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.6c01208", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-23" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/54", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "bam", "name": "BAM", "summary": "BAM generates and ranks structural candidates for suspect metabolites by applying biotransformation rules to known anchor structures.", "links": { "primary": "https://github.com/HassounLab/BAM", "repository": "https://github.com/HassounLab/BAM", "documentation": "https://github.com/HassounLab/BAM#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "annotation_identification", "secondary": [ "molecular_networking" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "command_line" ], "access": { "model": "source_available", "notes": "Public research code; no repository-wide license grant was established. PROXIMAL2, GNN-SOM and reaction databases require separate installation and terms review." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "molecular_structures", "statistical_results" ], "other_input_formats": [ "Reaction/metabolite CSV data and mass/SMILES anchor-suspect pairs" ], "other_output_formats": [ "Generated and ranked candidate structures with evaluation artifacts" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-09-24", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "BAM generates and ranks structural candidates for suspect metabolites by applying biotransformation rules to known anchor structures. It combines reaction-derived operators and site-of-metabolism prediction with anchor-suspect relationships from molecular networks.", "scope": { "designed_for": [ "BAM generates and ranks structural candidates for suspect metabolites by applying biotransformation rules to known anchor structures." ], "not_designed_for": [ "Raw spectral network construction or confirmed structural identification without validating the generated candidate and proposed transformation." ], "requirements": [ "Separate PROXIMAL2 and GNN-SOM installations/environments, configured reaction/metabolite CSV tables and anchor-suspect pairs containing masses and known anchor SMILES." ], "considerations": [ "Reaction coverage, anchor correctness and site-of-metabolism predictions constrain candidate generation. KEGG and RetroRules examples retain their own data terms." ] }, "common_uses": [ "BAM generates and ranks structural candidates for suspect metabolites by applying biotransformation rules to known anchor structures." ], "publications": [ { "doi": "10.1021/acs.analchem.4c01565", "type": "primary", "citation": "Molecular Structure Discovery for Untargeted Metabolomics Using Biotransformation Rules and Global Molecular Networking. Analytical Chemistry (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "biopan", "name": "BioPAN", "summary": "LIPID MAPS web tool for exploring changes in mammalian lipid pathways and predicting relative reaction or enzyme activity from quantitative lipidomics data.", "links": { "primary": "https://www.lipidmaps.org/tool/biopan/", "documentation": "https://www.lipidmaps.org/tool/biopan/doc/index.html" }, "resource_types": [ "web_service" ], "functions": { "primary": "pathway_interpretation", "capabilities": [ "pathway_enrichment", "network_visualization" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted LIPID MAPS tool requiring no installation. Documentation states that analysis links remain available for 30 days; export results needed for long-term records." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official service documentation and dedicated tool citation reviewed on 2026-09-11. Included for downstream MS lipidomics interpretation. No user dataset was uploaded or enrichment analysis run." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "lipidomics" ], "data": { "input_types": [ "feature_table", "compound_list" ], "output_types": [ "pathway_data", "statistical_results", "report" ], "input_formats": [ "csv_tsv" ], "output_formats": [ "json" ], "other_output_formats": [ "JPEG/PNG/SVG pathway graphs", "Text reaction scores and result tables" ] }, "publications": [ { "citation": "Gaud C, Sousa BC, Nguyen A, et al. BioPAN: a web-based tool to explore mammalian lipidome metabolic pathways on LIPID MAPS. F1000Research 10, 4 (2021).", "type": "primary" } ], "related_tools": [ { "slug": "lipid-maps", "relationship": "part_of", "note": "Hosted by the LIPID MAPS Lipidomics Gateway." }, { "slug": "lipidlynxx", "relationship": "integration", "note": "Optional nomenclature conversion is documented in the BioPAN input workflow." } ], "description": "BioPAN compares quantitative mammalian lipidomics data within a curated reaction network. It highlights predicted pathway changes and associated enzymes, with optional LipidLynxX nomenclature conversion. These are abundance-based inferences, not direct flux measurements.", "common_uses": [ "Compare lipid-class and molecular-species pathway patterns.", "Prioritize possible enzyme-associated changes for follow-up." ], "scope": { "designed_for": [ "Pathway exploration from quantitative mammalian lipidomics." ], "not_designed_for": [ "Direct measurement of enzyme activity or metabolic flux.", "Oxidized-lipid analysis under the documented input restrictions." ], "requirements": [ "CSV lipid concentrations with at least two conditions and two samples per condition." ], "considerations": [ "For sum-composition annotations, input order can affect which reaction combinations are retained.", "Review nomenclature conversion and annotation resolution before interpreting the graph." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "blink", "name": "BLINK", "summary": "A Python package for efficient cosine-based similarity scoring and matching of large collections of fragmentation mass spectra, including analog-search workflows.", "links": { "primary": "https://github.com/biorack/blink", "repository": "https://github.com/biorack/blink", "documentation": "https://github.com/biorack/blink#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "secondary": [ "annotation_identification" ], "capabilities": [ "library_matching", "ms2_processing" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "command_line", "notebook" ], "access": { "model": "open_source", "notes": "The repository includes a Berkeley Lab redistribution license with source/binary notice and non-endorsement conditions. The command-line and pretrained-model interfaces are labelled experimental.", "license": "BSD-3-Clause-LBNL" }, "technical": { "operating_systems": [ "linux", "macos", "windows" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra", "spectral_library" ], "output_types": [ "statistical_results" ], "input_formats": [ "mgf", "mzml" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1038/s41598-023-40496-9", "type": "primary", "citation": "BLINK enables ultrafast tandem mass spectrometry cosine similarity scoring. Scientific Reports (2023)." } ], "common_uses": [ "Calculate large numbers of MS/MS similarity scores efficiently.", "Perform library or analog searches using fragmentation spectra." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-01-16", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Fast spectral similarity and analog-search calculations on MS/MS data." ], "considerations": [ "Similarity thresholds and interpretation remain dataset- and use-case-dependent.", "The repository describes its command-line interface as experimental." ], "not_designed_for": [ "Chromatographic feature extraction or definitive structure identification from similarity alone." ], "requirements": [ "Python with the supplied conda environment; query and reference spectra. REM-BLINK additionally needs polarity-specific models and the same ordered mass-difference list used for training." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "description": "BLINK computes cosine-based spectral similarities and matching-ion counts using a discretized representation of fragmentation spectra. Its experimental REM-BLINK interface also supports analog comparisons using specified mass differences and pretrained regression models." }, { "$schema": "../../schemas/tool.schema.json", "slug": "breathxplorer", "name": "BreathXplorer", "summary": "breathXplorer processes high-resolution breath mass-spectrometry data with feature extraction, alignment and breath-recognition functions.", "links": { "primary": "https://github.com/HuanLab/breathXplorer", "repository": "https://github.com/HuanLab/breathXplorer", "documentation": "https://github.com/HuanLab/breathXplorer#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [ "spectral_analysis" ], "capabilities": [ "peak_detection", "retention_time_alignment", "ms2_processing" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "volatilomics" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ], "package_managers": [ "PyPI" ] }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "feature_table" ], "other_input_formats": [ "mzML or mzXML breath-MS files" ], "other_output_formats": [ "CSV feature tables and MGF tandem spectra" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-07-22", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "breathXplorer processes high-resolution breath mass-spectrometry data with feature extraction, alignment and breath-recognition functions. It produces sample-level or aligned feature tables and can export tandem spectra for further annotation.", "scope": { "designed_for": [ "breathXplorer processes high-resolution breath mass-spectrometry data with feature extraction, alignment and breath-recognition functions." ], "not_designed_for": [ "Automatic clinical interpretation of breath biomarkers or a validated replacement for every LC-MS processing workflow." ], "requirements": [ "Python 3.7-3.10 with the documented dependencies and mzML or mzXML breath-MS files; configure the expected breath count for methods that require it." ], "considerations": [ "Breath-recognition settings and the selected extraction algorithm affect features and alignment. Review breath segmentation and feature quality before biological interpretation." ] }, "common_uses": [ "breathXplorer processes high-resolution breath mass-spectrometry data with feature extraction, alignment and breath-recognition functions." ], "publications": [ { "doi": "10.1021/jasms.4c00152", "type": "primary", "citation": "BreathXplorer: Processing Online Breathomics Data Generated from Direct Analysis Using High-Resolution Mass Spectrometry. Journal of the American Society for Mass Spectrometry (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "camera", "name": "CAMERA", "summary": "Group and annotate related LC-MS signals after XCMS peak detection.", "description": "CAMERA uses chromatographic relationships and isotope/adduct rules to organize peaks into compound-related groups. This can reduce redundant signals before interpretation.", "links": { "primary": "https://bioconductor.org/packages/CAMERA/", "documentation": "https://bioconductor.org/packages/CAMERA/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "adduct_isotope_grouping" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Open-source software; use the official installation, license and citation guidance for the selected release." }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "feature_table", "compound_list" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "XCMS peak-list objects" ], "other_output_formats": [ "R annotation tables and pseudospectra" ] }, "publications": [ { "citation": "Kuhl C, Tautenhahn R, Böttcher C, et al. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem 84, 283-289 (2012).", "type": "primary", "doi": "10.1021/ac202450g" } ], "common_uses": [ "Annotate isotopes and adducts in XCMS peak lists.", "Investigate groups of coeluting ion features." ], "scope": { "designed_for": [ "Annotate isotopes and adducts in XCMS peak lists." ], "not_designed_for": [ "Confirming a unique chemical structure from an adduct assignment." ], "requirements": [ "XCMS-generated peak lists and compatible R/Bioconductor versions." ], "considerations": [ "Coelution and correlation are imperfect evidence; inspect ambiguous groups and ionization-specific rules." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "xcms", "relationship": "complementary" }, { "slug": "ramclustr", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "canopus", "name": "CANOPUS", "summary": "A SIRIUS-integrated machine-learning method and webservice for predicting compound classes directly from high-resolution MS/MS data without requiring an exact database structure match.", "description": "CANOPUS uses molecular fingerprints predicted from tandem mass spectra to infer hierarchical compound classes. Because it predicts class membership without requiring the exact compound to exist in a spectral or structure database, it is particularly useful for characterizing unknown metabolite features when precise structure identification is not possible. CANOPUS is currently accessed as a webservice-backed component of SIRIUS.", "links": { "primary": "https://v6.docs.sirius-ms.io/", "documentation": "https://v6.docs.sirius-ms.io/methods-background/" }, "resource_types": [ "web_service" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "compound_class_prediction", "substructure_prediction" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "desktop_gui", "command_line", "api" ], "access": { "model": "free_academic", "notes": "CANOPUS is accessed through SIRIUS web services and is currently free for academic use under the SIRIUS licensing terms." }, "publications": [ { "doi": "10.1038/s41587-020-0740-8", "citation": "Dührkop K, Nothias LF, Fleischauer M, et al. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol 39, 462-471 (2021).", "type": "primary" } ], "common_uses": [ "Assign hierarchical chemical classes to MS/MS features lacking confident exact structure annotations.", "Summarize chemical diversity across large untargeted metabolomics datasets at class level.", "Provide structural context for features absent from spectral and molecular-structure reference databases." ], "scope": { "designed_for": [ "Compound-class prediction from accurate-mass tandem mass spectra." ], "requirements": [ "SIRIUS-compatible high-resolution MS/MS data and access to the SIRIUS webservice." ], "considerations": [ "CANOPUS predicts compound classes rather than exact structures; results should not be reported as compound-level identifications.", "Class confidence and resolution depend on the information contained in the spectrum and the trained classification hierarchy.", "Inspect predicted class probabilities and hierarchy alongside the spectrum; retain the SIRIUS project and class export." ], "not_designed_for": [ "Confirmation of an exact compound structure or discrimination of stereoisomers." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "sirius", "relationship": "part_of" }, { "slug": "csi-fingerid", "relationship": "uses" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "api_available": true }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list", "report" ], "input_formats": [ "mzml", "mzxml", "mgf", "msp" ], "output_formats": [ "csv_tsv", "json" ], "other_input_formats": [ "SIRIUS project with predicted fingerprints" ], "other_output_formats": [ "SIRIUS class prediction tables" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "cardinal", "name": "Cardinal", "summary": "Open-source R/Bioconductor toolbox for preprocessing, statistical analysis, segmentation, classification and visualization of mass spectrometry imaging data.", "description": "Cardinal provides reproducible analysis of mass spectrometry imaging experiments, including preprocessing, visualization, spatial segmentation, classification and multivariate analysis. Cardinal v3 supports a broad range of MSI workflows and is designed for programmatic analysis in R.", "links": { "primary": "https://cardinalmsi.org/", "repository": "https://github.com/Cardinalverse/Cardinal", "documentation": "https://cardinalmsi.org/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "secondary": [ "preprocessing", "visualization_reporting" ], "capabilities": [ "multivariate_statistics" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "Artistic-2.0", "notes": "Install through Bioconductor with an R version compatible with the selected release. The current source declares Artistic-2.0 or the accompanying license file; package dependencies and versions should be recorded for reproducibility." }, "data": { "input_types": [ "imaging_data" ], "output_types": [ "imaging_data", "statistical_results" ], "input_formats": [ "imzml" ], "other_output_formats": [ "R analysis objects and statistical or image outputs" ] }, "publications": [ { "citation": "Bemis KA, Föll MC, Guo D, et al. Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis. Nature Methods 20, 1883–1886 (2023).", "type": "methods", "doi": "10.1038/s41592-023-02070-z" }, { "citation": "Bemis KD, Harry A, Eberlin LS, et al. Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments. Bioinformatics 31, 2418–2420 (2015).", "type": "primary", "doi": "10.1093/bioinformatics/btv146" } ], "common_uses": [ "Preprocess and visualize MSI datasets.", "Segment tissues by chemical composition.", "Perform classification and multivariate analysis of MSI experiments." ], "scope": { "designed_for": [ "Reproducible statistical analysis of MALDI- and DESI-based MSI datasets." ], "not_designed_for": [ "Definitive metabolite identification solely from ion images or statistical classification." ], "requirements": [ "Compatible imaging-MS data, spatial coordinates and an R/Bioconductor environment.", "Biological sample metadata and an analysis design appropriate to spatial measurements." ], "considerations": [ "Pixels from one tissue are not independent biological replicates; preserve sample-level grouping during inference and validation.", "Interpretation depends on acquisition quality, preprocessing choices and spatial experimental design." ] }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official package metadata and original/version-3 publication identities verified on 2026-09-10. No independent software benchmark or developer verification performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_editorial_review": "2026-09-10", "last_verified": "2026-09-10" }, "related_tools": [ { "slug": "metaspace", "relationship": "complementary", "note": "Spatial statistical analysis and molecular annotation address different stages; no automatic integration is implied." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ccsfind", "name": "CCSfind", "summary": "CCSfind builds experimentally derived small-molecule databases from LC-drift-tube-ion-mobility-MS data.", "links": { "primary": "https://github.com/sangeeta97/ccs_find", "repository": "https://github.com/sangeeta97/ccs_find", "documentation": "https://github.com/sangeeta97/ccs_find#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "secondary": [ "annotation_identification", "data_management_sharing" ], "capabilities": [ "adduct_isotope_grouping" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "raw_instrument_data", "compound_list" ], "output_types": [ "feature_table", "compound_list" ], "other_input_formats": [ "Profile LC-DTIM-MS mzML, formulas and calibration parameters" ], "other_output_formats": [ "Experimental ion/CCS database entries with SQL export" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-02-02", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "CCSfind builds experimentally derived small-molecule databases from LC-drift-tube-ion-mobility-MS data. It searches supplied formulas and ion species, checks isotopologue evidence and separates chromatographic or mobility-resolved candidates for database export.", "scope": { "designed_for": [ "CCSfind builds experimentally derived small-molecule databases from LC-drift-tube-ion-mobility-MS data." ], "not_designed_for": [ "Predicting CCS directly from an arbitrary molecular structure or universal support for all ion-mobility platforms." ], "requirements": [ "Python 3.9 dependencies or a compatible GUI build, profile-mode mzML, target formulas/ion species, drift gas and single-field calibration coefficients Tfix and Beta." ], "considerations": [ "The documented workflow targets drift-tube data. Calibration, isotope tolerances and peak-width settings affect database entries and separation of isomers/protomers." ] }, "common_uses": [ "CCSfind builds experimentally derived small-molecule databases from LC-drift-tube-ion-mobility-MS data." ], "publications": [ { "doi": "10.1002/jms.5040", "type": "primary", "citation": "CCSfind: A tool for chemically informed LC‐IM‐MS database building. Journal of Mass Spectrometry (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "cfm-id", "name": "CFM-ID", "summary": "Predict tandem mass spectra, annotate fragments and rank candidate structures for small molecules.", "description": "CFM-ID combines learned fragmentation and rule-based components. The version 4 web workflow focuses on ESI-QTOF MS/MS and provides predicted-versus-observed spectral comparisons.", "links": { "primary": "https://cfmid.wishartlab.com/help", "documentation": "https://cfmid.wishartlab.com/help" }, "resource_types": [ "web_service" ], "functions": { "primary": "annotation_identification", "capabilities": [ "in_silico_spectra", "structure_database_search" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app", "command_line" ], "access": { "model": "free_web_service", "notes": "Free web access and downloadable components; consult individual code, model and database terms." }, "data": { "input_types": [ "spectra", "molecular_structures" ], "output_types": [ "spectra", "compound_list" ], "input_formats": [ "smiles_inchi", "other" ], "output_formats": [ "other" ], "other_output_formats": [ "Predicted peak lists and ranked candidates" ], "other_input_formats": [ "Structure strings or formatted spectral peak lists" ] }, "publications": [ { "citation": "Wang F, Allen D, Tian S, et al. CFM-ID 4.0 - a web server for accurate MS-based metabolite identification. Nucleic Acids Res 50, W165-W174 (2022).", "type": "primary", "doi": "10.1093/nar/gkac383" } ], "common_uses": [ "Compare an observed spectrum with candidate structures.", "Predict fragmentation for a supplied molecular structure." ], "scope": { "designed_for": [ "Compare an observed spectrum with candidate structures." ], "not_designed_for": [ "Proving stereochemistry or identity from a top-ranked candidate alone." ], "requirements": [ "A molecular structure for prediction, or a spectrum with appropriate precursor and collision-energy information for annotation." ], "considerations": [ "Instrument and collision-energy differences affect agreement. The command-line image does not include all web-server spectral databases." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metfrag", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "chebi", "name": "ChEBI", "summary": "Look up curated chemical entities, names and ontology relationships.", "description": "ChEBI connects chemical structures to classes and biological roles through a maintained ontology, supporting consistent naming and interoperability.", "links": { "primary": "https://www.ebi.ac.uk/chebi/", "documentation": "https://www.ebi.ac.uk/chebi/" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Open-access EBI database; use the resource policies for redistribution." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "sdf", "other" ], "other_input_formats": [ "Text, formula or structure queries" ], "other_output_formats": [ "SDF and ontology downloads" ] }, "publications": [ { "doi": "10.1093/nar/gkaf1271", "citation": "Malik A, Arsalan M, Moreno C, et al. ChEBI: re-engineered for a sustainable future. Nucleic Acids Res 54, D1768-D1778 (2026).", "type": "primary" } ], "resource_citations": [ { "citation": "ChEBI team. Chemical Entities of Biological Interest: database and ontology documentation. EMBL-EBI.", "type": "documentation", "url": "https://www.ebi.ac.uk/chebi/" } ], "common_uses": [ "Map metabolite names to stable chemical identifiers.", "Group compounds by chemical class or biological role." ], "scope": { "designed_for": [ "Map metabolite names to stable chemical identifiers." ], "not_designed_for": [ "Direct identification of a measured mass-spectrometry feature." ], "requirements": [ "A chemical name, formula, identifier or structure query." ], "considerations": [ "An ontology role describes curated knowledge, not evidence that a function occurs in the current experiment." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "pubchem", "relationship": "complementary" }, { "slug": "reactome", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "chemprop", "name": "Chemical Proportionality (ChemProp)", "summary": "Prioritize putative transformations between molecular-network features using abundance changes across ordered samples.", "description": "Chemical Proportionality adds abundance-pattern context to molecular networks. The current ChemProp2 web app includes the original log-ratio method for two-point comparisons and correlation-based scoring for three or more sequential observations. Scores help select feature pairs for investigation; they do not establish a biochemical reaction. This is distinct from the unrelated Chemprop molecular-property prediction package.", "links": { "primary": "https://chemprop.gnps2.org/", "documentation": "https://github.com/Functional-Metabolomics-Lab/ChemProp-Web-App/blob/main/Home.py", "repository": "https://github.com/Functional-Metabolomics-Lab/ChemProp-Web-App" }, "resource_types": [ "web_service" ], "functions": { "primary": "statistics", "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "The source is BSD-3-Clause licensed. The app can be hosted locally with Python and Streamlit; the public instance is linked above. No analysis job was run during editorial review." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_output_formats": [ "Interactive app views of feature-pair scores and network context" ], "input_formats": [ "csv_tsv" ], "other_input_formats": [ "TXT and XLSX uploads supported by the app", "GNPS2 feature-based molecular networking task identifier", "Network edge table for provided-edge and cascade modes" ] }, "common_uses": [ "Prioritize molecular-network feature pairs whose abundance patterns suggest a potential transformation.", "Compare temporal or spatial abundance trends among connected features." ], "scope": { "designed_for": [ "Ordered non-targeted LC-MS/MS feature-abundance data and molecular-network context." ], "not_designed_for": [ "Raw-data peak picking or conclusive assignment of reaction mechanisms and metabolite identities." ], "requirements": [ "Feature quantification and sample metadata with consistent sample identifiers and an ordered comparison variable.", "An edge table for provided-edge and cascade analyses; annotation tables are optional.", "Select the scoring mode appropriate to two-point versus longer sequential comparisons." ], "considerations": [ "Anti-correlation and inferred direction are prioritization evidence, not proof of conversion or causation.", "Sample preparation, normalization, missing values and experimental ordering affect abundance patterns.", "Spectrum Resolver and ModiFinder links require USIs supplied by the GNPS2 task-ID input route; manual uploads do not provide that integration.", "The original publication describes ChemProp1; it should not be treated as validation of every feature in ChemProp2." ] }, "publications": [ { "citation": "Petras D, Caraballo-Rodríguez AM, Jarmusch AK, et al. Chemical Proportionality within Molecular Networks. Analytical Chemistry 93, 12833–12839 (2021).", "type": "primary", "doi": "10.1021/acs.analchem.1c01520", "pmid": "34533933" } ], "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Listed as a standalone Chemical Proportionality app in the MetaboApps ecosystem." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Accepts a GNPS2 FBMN task identifier to retrieve feature tables, metadata and network context." }, { "slug": "modifinder", "relationship": "integration", "note": "The task-ID route supports USI-based follow-up with ModiFinder; manual uploads do not." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official app source, repository license, GNPS2 app listing and original publication metadata reviewed on 2026-09-10. No independent benchmark or developer verification performed. ChemProp2 manuscript attribution remains under review; generic FBMN statistics citations were not copied as ChemProp core publications." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "maintenance": { "status": "active", "last_checked": "2026-09-10" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "chemwalker", "name": "ChemWalker", "summary": "Rank candidate molecular structures by propagating spectral-library annotation evidence through molecular networks using random walks.", "platforms": [ "lc_ms" ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official ChemWalker repository and GNPS2 workflow input definition reviewed; primary DOI/title/year verified through Crossref on 2026-09-23. No software execution or developer verification." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23", "last_editorial_review": "2026-09-23" }, "maintenance": { "status": "unclear", "last_checked": "2026-09-23" }, "description": "ChemWalker combines molecular-network context with candidate structures evaluated by in-silico fragmentation. Its Python implementation uses MetFrag, while a GNPS2 workflow accepts a networking task and a selected molecular family. Propagated rankings guide annotation follow-up rather than confirming an identity.", "links": { "primary": "https://github.com/computational-chemical-biology/ChemWalker", "repository": "https://github.com/computational-chemical-biology/ChemWalker", "documentation": "https://github.com/Wang-Bioinformatics-Lab/Chemwalker_workflow/blob/master/workflowinput.yaml", "web_app": "https://gnps2.org/workflowinput?workflowname=chemwalker_nextflow_workflow" }, "resource_types": [ "programming_package", "workflow_pipeline" ], "interfaces": [ "python_package", "web_app" ], "functions": { "primary": "annotation_identification", "secondary": [ "molecular_networking" ], "capabilities": [ "in_silico_spectra" ] }, "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "Core Python package is BSD-3-Clause; MetFrag and workflow dependencies have their own requirements. Hosted GNPS2 execution requires an account and is restricted to academic/non-commercial use without a separate license." }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list", "report" ], "other_input_formats": [ "GNPS networking task ID and component index", "Ion mode, adduct and optional pipe-separated candidate database" ], "other_output_formats": [ "Candidate annotation rankings from the selected network component." ] }, "common_uses": [ "Prioritize candidate structures for unannotated members of a molecular family.", "Combine candidate fragmentation evidence with neighboring library annotations." ], "scope": { "designed_for": [ "Annotation propagation within MS/MS molecular networks." ], "not_designed_for": [ "Raw feature detection, quantitative statistics or confirmed identification without additional evidence." ], "requirements": [ "A compatible network task and selected molecular component; the GNPS2 form requests one component per run.", "Appropriate ion/adduct settings and a candidate structure database; local installation requires the documented environment and MetFrag." ], "considerations": [ "Incorrect seed annotations and missing candidate structures can affect propagated rankings.", "The repository reports testing with MetFrag 2.3; compatibility with newer dependencies was not execution-tested." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btad078", "type": "primary", "citation": "Borelli TC, Arini GS, Feitosa LGP, et al. Improving annotation propagation on molecular networks through random walks: introducing ChemWalker. Bioinformatics 39(3), btad078 (2023)." } ], "related_tools": [ { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "The GNPS2 workflow accepts FBMN task identifiers and a molecular component." }, { "slug": "metfrag", "relationship": "integration", "note": "Uses MetFrag for candidate in-silico fragmentation." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "cmmc-dashboard", "name": "CMMC Dashboard", "summary": "Visualize CMMC enrichment results alongside feature abundances, metadata and molecular-network information.", "links": { "primary": "https://cmmc-dashboard.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_CMMC_dashboard/" }, "resource_types": [ "web_service" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "metadata_contextualization", "interactive_dashboard", "network_visualization" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly hosted MetaboApp. GNPS2 infrastructure is open to the academic community; its documentation directs industry users to the maintainers or UCR Technology Transfer. Hosted access does not establish a software or reference-data license." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against the linked official app documentation on 2026-09-10. No standalone publication for this particular app was verified; the documentation citation identifies the reviewed implementation. Underlying method and application papers are not presented as dedicated app papers. No analysis job or independent benchmark was run; no developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "report", "statistical_results" ], "input_formats": [ "csv_tsv" ], "other_input_formats": [ "Completed CMMC enrichment and FBMN task IDs", "Sample metadata can also use Excel or TXT" ], "other_output_formats": [ "Interactive box plots, UpSet plots, networks and microbial search views" ] }, "scope": { "designed_for": [ "Downstream exploration after CMMC enrichment and FBMN processing." ], "not_designed_for": [ "Replacing CMMC enrichment or establishing the producer of a metabolite in a mixed sample." ], "requirements": [ "Completed CMMC enrichment and FBMN task identifiers, plus sample metadata.", "Metadata sample names must exactly match the FBMN quantification table." ], "considerations": [ "Interpret source annotations and microbeMASST matches as supporting context rather than proof of origin.", "Quantitative comparisons depend on upstream processing and appropriate sample grouping." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Standalone MetaboApp that can be launched directly or from a GNPS2 task." }, { "slug": "gnps", "relationship": "integration", "note": "Consumes GNPS2 tasks or ecosystem data." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Uses results from a completed FBMN task." }, { "slug": "cmmc-kb", "relationship": "integration", "note": "Provides the enrichment knowledge context." }, { "slug": "microbemasst", "relationship": "integration", "note": "Supports microbial spectral-context searches." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "publications": [], "resource_citations": [ { "type": "documentation", "citation": "GNPS2 Documentation. CMMC Dashboard. Accessed 10 September 2026.", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_CMMC_dashboard/" } ], "description": "CMMC Dashboard brings together CMMC enrichment, FBMN abundances and sample metadata. It provides group comparisons, overlap views, molecular-network displays and microbeMASST links for exploring microbial metabolite hypotheses.", "common_uses": [ "Inspect microbial-metabolite annotations alongside sample abundances.", "Explore overlap between source categories and potential microbial contexts." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "cmmc-kb", "name": "CMMC-KB", "summary": "A community-curated knowledgebase for microbial metabolites that links reference MS/MS evidence, microbial producers, annotations, and GNPS2 analysis resources.", "description": "CMMC-KB is part of the Collaborative Microbial Metabolite Center infrastructure for community curation of microbial small molecules. It is designed to consolidate evidence connecting microbial metabolites with spectra, structures, producers, and contextual metadata, and to make that knowledge reusable in GNPS2 analyses such as molecular-network enrichment and microbial contextualization. The associated scientific description is currently a 2026 preprint.", "links": { "primary": "https://cmmc-kb.gnps2.org/", "web_app": "https://cmmc-kb.gnps2.org/", "documentation": "https://pubmed.ncbi.nlm.nih.gov/41659575/" }, "resource_types": [ "database_knowledgebase", "web_service" ], "functions": { "primary": "annotation_identification", "secondary": [ "reference_data_search", "data_management_sharing" ], "capabilities": [ "library_matching", "metadata_contextualization", "provenance_tracking" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.64898/2026.01.24.701521", "citation": "Mannochio-Russo H, Goncalves Nunes WD, Xing S, et al. Community Curation of Microbial Metabolites Enables Biological Insights of Metabolomics Data. bioRxiv (2026). Preprint; not peer reviewed.", "type": "primary" } ], "common_uses": [ "Curate and retrieve microbial-metabolite knowledge linking spectra, chemical annotations, and producer context.", "Enrich GNPS2 analyses with community-curated microbial metabolite evidence.", "Support community deposition and validation of microbial metabolite annotations.", "Download curated spectra and metadata for reuse while keeping their supporting evidence attached." ], "scope": { "designed_for": [ "Community knowledge curation and downstream interpretation of microbial metabolomics data." ], "requirements": [ "Curated metabolite/spectral evidence for deposition or GNPS2-compatible analysis results for downstream use." ], "considerations": [ "The main scientific description is currently a preprint and the knowledgebase is actively expanding.", "Curated evidence can span different levels of structural and biological confidence; users should inspect supporting evidence for individual annotations.", "Inspect the evidence and source category for each entry; host, food, drug and exposure-related molecules can also be represented." ], "not_designed_for": [ "Treating every curated occurrence or source annotation as experimentally proven microbial biosynthesis." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "microbemasst", "relationship": "complementary" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser" ], "api_available": false }, "data": { "input_types": [ "spectra", "molecular_structures", "sample_metadata" ], "output_types": [ "spectra", "compound_list", "sample_metadata", "report" ], "input_formats": [ "mgf", "smiles_inchi", "csv_tsv" ], "output_formats": [ "csv_tsv", "mgf" ], "other_input_formats": [ "Spectrum identifiers and curated compound metadata" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "coconut", "name": "COCONUT", "summary": "Search an aggregated, curated collection of natural-product structures and source records.", "description": "COCONUT brings together natural-product collections with community curation and audit trails, supporting candidate lookup and computational analyses.", "links": { "primary": "https://coconut.naturalproducts.net/", "documentation": "https://steinbeck-lab.github.io/coconut/introduction.html" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Open platform with downloadable data; review database and source-collection licenses." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "sdf", "other" ], "other_input_formats": [ "Text or structure queries" ], "other_output_formats": [ "Structure records and database exports" ] }, "publications": [ { "citation": "Chandrasekhar V, Rajan K, Kanakam SRS, et al. COCONUT 2.0: a comprehensive overhaul and curation of the collection of open natural products database. Nucleic Acids Res 53, D634-D643 (2025).", "type": "primary", "doi": "10.1093/nar/gkae1063" } ], "common_uses": [ "Search natural-product structures and related compounds.", "Download a defined release for reproducible candidate screening." ], "scope": { "designed_for": [ "Search natural-product structures and related compounds." ], "not_designed_for": [ "A uniform set of experimentally validated spectral identifications." ], "requirements": [ "A structure or text query, or a downloaded release for local use." ], "considerations": [ "Source collections differ in provenance and detail; inspect source links and record the release used." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "lotus", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "concise", "name": "ConCISE", "summary": "ConCISE propagates consensus chemical-class annotations by combining molecular-network relationships, library matches and in-silico class predictions.", "links": { "primary": "https://github.com/zquinlan/concise", "repository": "https://github.com/zquinlan/concise", "documentation": "https://zquinlan.github.io/conCISE/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "molecular_networking", "secondary": [ "annotation_identification" ], "capabilities": [ "compound_class_prediction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "command_line", "desktop_gui", "notebook" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "compound_list", "statistical_results" ], "output_types": [ "compound_list", "report" ], "other_input_formats": [ "GNPS library/task results, CANOPUS TSV and network information" ], "other_output_formats": [ "Consensus chemical-class annotation tables" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-11-15", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "ConCISE propagates consensus chemical-class annotations by combining molecular-network relationships, library matches and in-silico class predictions. It accepts GNPS and CANOPUS-derived results through graphical, command-line or notebook workflows.", "scope": { "designed_for": [ "ConCISE propagates consensus chemical-class annotations by combining molecular-network relationships, library matches and in-silico class predictions." ], "not_designed_for": [ "Confirmed molecular structures or class assignments independent of the quality of the underlying network and source annotations." ], "requirements": [ "Compatible library-match/GNPS task information, CANOPUS summary and network-node information with consistent feature identifiers; choose ClassyFire or NPClassifier ontology inputs." ], "considerations": [ "README documents missing ClassyFire fields from GNPS and provides NPClassifier/manual-merge workarounds. Check ontology consistency and consensus thresholds; the macOS GUI is deprecated." ] }, "common_uses": [ "ConCISE propagates consensus chemical-class annotations by combining molecular-network relationships, library matches and in-silico class predictions." ], "publications": [ { "doi": "10.3390/metabo12121275", "type": "primary", "citation": "ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction. Metabolites (2022)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "conjugated-metabolome-explorer", "name": "Conjugated Metabolome Explorer", "summary": "Search public LC-MS/MS evidence for potential conjugated forms of a molecule supplied as a SMILES structure.", "links": { "primary": "https://conjugated-metabolome.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Conjugated_Metabolome_Explorer/" }, "resource_types": [ "web_service" ], "functions": { "primary": "reference_data_search", "capabilities": [ "repository_scale_search", "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "The documentation states that the public app requires no login or registration. Publicly hosted MetaboApp. GNPS2 infrastructure is open to the academic community; its documentation directs industry users to the maintainers or UCR Technology Transfer. Hosted access does not establish a software or reference-data license." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against the linked official app documentation on 2026-09-10. No standalone publication for this particular app was verified; the documentation citation identifies the reviewed implementation. Underlying method and application papers are not presented as dedicated app papers. No analysis job or independent benchmark was run; no developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "compound_list", "report" ], "other_input_formats": [ "SMILES string and minimum occurrence-frequency setting" ], "other_output_formats": [ "Interactive conjugation table, mass-shift plots and spectral links" ] }, "scope": { "designed_for": [ "Structure-led exploration of potential conjugated metabolites in public LC-MS/MS data." ], "not_designed_for": [ "Confirming a conjugation site, reaction pathway or enzyme from a mass shift alone." ], "requirements": [ "A valid SMILES structure and a selected minimum occurrence frequency." ], "considerations": [ "A delta-mass inference has different support from a direct spectral match.", "Occurrence counts reflect repository sampling and should not be treated as quantitative biological prevalence.", "Inspect the linked spectra before promoting a proposed conjugate into an identification." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Standalone MetaboApp that can be launched directly or from a GNPS2 task." }, { "slug": "gnps", "relationship": "integration", "note": "Consumes GNPS2 tasks or ecosystem data." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "description": "Conjugated Metabolome Explorer combines spectral matches and mass-shift relationships to investigate possible metabolite conjugations. Results distinguish spectral evidence from components inferred by a mass difference and link to supporting spectra.", "publications": [], "resource_citations": [ { "citation": "GNPS2 Documentation. Conjugated Metabolome Explorer. Accessed 10 September 2026.", "type": "documentation", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Conjugated_Metabolome_Explorer/" } ], "common_uses": [ "Explore candidate conjugations starting from a known structure.", "Inspect spectral support for recurrent mass shifts in public data." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "cordbat", "name": "CordBat", "summary": "An R package implementing concordance-based batch-effect correction for large-scale metabolomics studies.", "links": { "primary": "https://github.com/BorchLab/CordBat", "repository": "https://github.com/BorchLab/CordBat", "documentation": "https://github.com/BorchLab/CordBat/blob/main/vignettes/CordBat.Rmd" }, "resource_types": [ "programming_package" ], "functions": { "primary": "normalization", "secondary": [], "capabilities": [ "batch_effect_correction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "Package DESCRIPTION, LICENSE and LICENSE.md specify MIT terms; this R implementation is distinguished from the original method publication." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "R abundance matrix with batch/group vectors and reference-batch identifier" ], "other_output_formats": [ "Corrected R abundance matrix and fitted correction results" ] }, "publications": [ { "doi": "10.1021/acs.analchem.2c05748", "type": "primary", "citation": "Concordance-Based Batch Effect Correction for Large-Scale Metabolomics. Analytical Chemistry (2023)." } ], "common_uses": [ "Correct batch effects in large metabolomics datasets while preserving concordance structure." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-05-08", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "CordBat implements concordance-based correction of batch effects in quantified metabolomics matrices. Its R interface takes batch and biological-group information and a selected reference batch, returning corrected measurements for further analysis.", "scope": { "designed_for": [ "An R package implementing concordance-based batch-effect correction for large-scale metabolomics studies." ], "not_designed_for": [ "Raw feature extraction or reliable separation of biological and technical effects in an unidentifiable, fully confounded design." ], "requirements": [ "R, an appropriately transformed numeric sample-by-feature matrix, batch labels and the biological grouping/reference-batch information expected by the documented function." ], "considerations": [ "Check matrix orientation and transformation, reference-batch choice and preservation of biological contrasts. A better batch-mixing plot alone does not establish a scientifically valid correction." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "corromics", "name": "CorrOmics", "summary": "Explore associations between paired metabolomics and other omics feature tables and export correlation networks.", "description": "CorrOmics links metabolite abundances with microbial or other omics measurements using sample-mapping metadata. It provides correlation-network exploration and downloadable edges; inferred associations do not establish biological causation. Hosted and local execution routes are documented.", "links": { "primary": "https://corromics.gnps2.org/", "documentation": "https://github.com/Functional-Metabolomics-Lab/Corromics/blob/main/Home.py", "repository": "https://github.com/Functional-Metabolomics-Lab/Corromics" }, "resource_types": [ "web_service" ], "functions": { "primary": "multiomics_integration", "secondary": [ "statistics", "visualization_reporting" ], "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public web app with documented Python/Streamlit local setup and a Windows installer route. No explicit repository license was established; public source availability is not an open-source license claim." }, "publications": [], "resource_citations": [ { "type": "documentation", "citation": "CorrOmics official documentation. Accessed 24 September 2026.", "url": "https://github.com/Functional-Metabolomics-Lab/Corromics/blob/main/Home.py" } ], "maintenance": { "status": "unclear", "last_checked": "2026-09-24" }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation reviewed on 2026-09-24. No dedicated publication verified; no analysis execution or independent benchmark claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-24", "updated_at": "2026-09-24", "last_verified": "2026-09-24", "last_editorial_review": "2026-09-24" }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Paired omics abundance tables and sample-mapping metadata" ], "output_formats": [ "csv_tsv" ], "other_output_formats": [ "GraphML network for Cytoscape" ] }, "common_uses": [ "Explore metabolite-microbe abundance associations.", "Export cross-omics association networks for inspection in Cytoscape." ], "scope": { "designed_for": [ "Exploratory association analysis of matched metabolomics and other omics samples." ], "not_designed_for": [ "Inferring causation, confirming metabolite identity or preprocessing raw MS files." ], "requirements": [ "Paired abundance tables and metadata with filename and ATTRIBUTE_Corromics_filename sample mappings.", "Local Python/Streamlit dependencies when running outside the hosted service." ], "considerations": [ "The documentation limits hosted analyses to one million correlations and recommends local execution above that.", "Compositional effects, confounders, missing values and repeated measures require study-specific assessment.", "Exploratory methods differ in interpretation and significance support; do not treat every association score as a corrected hypothesis test." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Listed in the GNPS2 MetaboApps ecosystem." }, { "slug": "cytoscape", "relationship": "integration", "note": "Exports GraphML for network visualization." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "crisp", "name": "CRISP", "summary": "CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training.", "links": { "primary": "https://github.com/vivekmathema/GCxGC-CRISP", "repository": "https://github.com/vivekmathema/GCxGC-CRISP", "documentation": "https://github.com/vivekmathema/GCxGC-CRISP#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "visualization_reporting" ], "capabilities": [ "feature_selection" ] }, "platforms": [ "gc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "desktop_gui" ], "access": { "model": "source_available", "notes": "Public research toolkit, but no repository-wide license grant was established. Review dependencies and supplied model/data terms before reuse." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "imaging_data" ], "output_types": [ "imaging_data", "statistical_results", "report" ], "other_input_formats": [ "Prepared GC×GC-TOFMS contour images and module-specific labels/models" ], "other_output_formats": [ "ROI/deep-stacked contours, generated/enhanced images and model artifacts" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2022-01-14", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training. Its graphical research toolkit works on prepared contour representations rather than directly identifying metabolites.", "scope": { "designed_for": [ "CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training." ], "not_designed_for": [ "Molecular identification from raw spectra or evidence that synthetic contours create independent biological samples." ], "requirements": [ "The documented deep-learning environment, prepared GC×GC contour images and class labels or model resources appropriate to the selected training/inference module." ], "considerations": [ "Keep augmentation and evaluation separated to avoid leakage. Contour preprocessing, selected regions and training-domain coverage determine classifier interpretation." ] }, "common_uses": [ "CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training." ], "publications": [ { "doi": "10.1093/bib/bbab550", "type": "primary", "citation": "CRISP: a deep learning architecture for GC × GC–TOFMS contour ROI identification, simulation and analysis in imaging metabolomics. Briefings in Bioinformatics (2022)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "csi-fingerid", "name": "CSI:FingerID", "summary": "A SIRIUS-integrated machine-learning method and webservice for predicting molecular fingerprints from MS/MS data and ranking candidate structures from molecular-structure databases.", "description": "CSI:FingerID predicts structural fingerprints from fragmentation-tree information and uses those fingerprints to search and rank candidate molecular structures in databases. In current SIRIUS releases it is a webservice-backed component of the structure-database search workflow rather than a standalone end-user application. It does not resolve stereochemistry and should be interpreted as ranked computational structure evidence rather than definitive identification.", "links": { "primary": "https://v6.docs.sirius-ms.io/", "documentation": "https://v6.docs.sirius-ms.io/methods-background/" }, "resource_types": [ "web_service" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning", "spectral_analysis" ], "capabilities": [ "structure_database_search", "substructure_prediction", "structure_elucidation" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "desktop_gui", "command_line", "api" ], "access": { "model": "free_academic", "notes": "CSI:FingerID is accessed through the SIRIUS web services; current webservice features are free for academic use, with separate non-academic licensing." }, "publications": [ { "doi": "10.1073/pnas.1509788112", "citation": "Dührkop K, Shen H, Meusel M, et al. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc Natl Acad Sci U S A 112, 12580-12585 (2015).", "type": "primary" } ], "common_uses": [ "Rank molecular-structure database candidates from accurate-mass MS/MS spectra.", "Predict molecular fingerprints that summarize structural properties inferred from fragmentation data.", "Provide structure-candidate rankings used by the SIRIUS COSMIC workflow and downstream interpretation.", "Prioritize database candidates for follow-up confirmation with standards or orthogonal measurements." ], "scope": { "designed_for": [ "Database-backed candidate structure identification of small molecules from informative, high-accuracy MS/MS spectra." ], "requirements": [ "SIRIUS-compatible high-resolution MS/MS data, a SIRIUS account/license for the webservice, and a molecular formula or formula candidates." ], "considerations": [ "CSI:FingerID does not determine stereochemistry and cannot identify structures absent from the searched databases.", "Ranking accuracy depends on spectral information content, molecular-formula quality, and candidate-database composition.", "Record the candidate database and formula settings, and inspect competing structures before interpreting the highest-ranked hit." ], "not_designed_for": [ "Unrestricted de novo generation of structures absent from the searched candidate database." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "sirius", "relationship": "part_of" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "api_available": true }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures", "compound_list", "report" ], "input_formats": [ "mzml", "mzxml", "mgf", "msp" ], "output_formats": [ "csv_tsv", "json" ], "other_input_formats": [ "SIRIUS project with formula candidates" ], "other_output_formats": [ "SIRIUS structure-candidate tables" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "cytoscape", "name": "Cytoscape", "summary": "Desktop network visualization and analysis for exploring MS molecular networks alongside feature annotations and sample metadata.", "links": { "primary": "https://cytoscape.org/", "documentation": "https://cytoscape.org/documentation_users.html", "download": "https://cytoscape.org/download.html" }, "resource_types": [ "software_application" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "network_visualization", "interactive_dashboard" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "LGPL-2.1", "notes": "Open-source desktop application. Use the installer and Java requirements for the selected release; apps can have separate licenses and compatibility requirements." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official project documentation and direct tool publications reviewed on 2026-09-12. Included for downstream MS metabolomics. No installation, dataset analysis or independent performance benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "data": { "input_types": [ "feature_table", "pathway_data" ], "output_types": [ "report" ], "other_output_formats": [ "Cytoscape session (.cys), network/table exports and figures" ], "other_input_formats": [ "Network edge lists, GraphML and node/edge attribute tables" ] }, "related_tools": [ { "slug": "gnps", "relationship": "integration", "note": "GNPS molecular networks are commonly exported to Cytoscape." }, { "slug": "gnps-cytoscape-bridge", "relationship": "integration", "note": "GNPS2 bridge can create ready-to-open Cytoscape sessions." } ], "description": "Cytoscape displays supplied nodes, edges and associated tables. In MS metabolomics it supports inspection and presentation of molecular networks generated upstream, with styles that encode sample groups or annotations. The desktop platform and its separately installed apps have distinct capabilities; a network viewer does not itself establish spectral matches.", "common_uses": [ "Style GNPS networks using study metadata.", "Inspect connected components and export publication figures." ], "scope": { "designed_for": [ "Exploration of supplied graphs and linked attribute tables." ], "not_designed_for": [ "Raw MS peak picking or confirmation of metabolite identity." ], "requirements": [ "An upstream network, stable node identifiers and matching metadata.", "A compatible desktop installation and any required apps." ], "considerations": [ "Check node identifiers when joining quantitative tables.", "Edge definitions and filtering come from upstream analysis; layout distances are not chemical evidence." ] }, "publications": [ { "citation": "Shannon P, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research 13, 2498-2504 (2003).", "doi": "10.1101/gr.1239303", "type": "primary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "dbnorm", "name": "dbnorm", "summary": "Compare statistical batch-correction models and inspect technical variation in metabolomics matrices.", "description": "dbnorm provides R functions for batch adjustment and diagnostic visualization. It includes empirical-Bayes and two-stage approaches and compares corrected data structure. Model selection does not establish that biological differences were preserved.", "links": { "primary": "https://github.com/NBDZ/dbnorm", "repository": "https://github.com/NBDZ/dbnorm", "documentation": "https://github.com/NBDZ/dbnorm" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "capabilities": [ "batch_effect_correction" ] }, "access": { "model": "open_source", "license": "GPL-3.0", "notes": "License checked against official package metadata or repository. Local setup requires the documented dependencies; no installation or hosted-service availability test was performed." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "CSV sample-by-feature matrix with batch labels" ], "other_output_formats": [ "Corrected CSV matrices and diagnostic PDF plots" ] }, "common_uses": [ "Inspect and compare batch-adjustment results." ], "scope": { "designed_for": [ "Compare statistical batch-correction models and inspect technical variation in metabolomics matrices." ], "not_designed_for": [ "Confirming compound identity or replacing study design and scientific review." ], "requirements": [ "Processed metabolomics data, batch labels and compatible R dependencies.", "CSV matrix with samples in rows, features in columns and batch labels; follow documented normalization/log2 preparation" ], "considerations": [ "Confounding between batch and biological groups requires assessment before correction.", "The documented setup includes archived R dependencies." ] }, "publications": [ { "doi": "10.1038/s41598-021-84824-3", "type": "primary", "citation": "DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies. Scientific Reports (2021)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate verified against official documentation, source/package metadata and primary-publication DOI/title/year on 2026-10-02. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "deepgcn-rt", "name": "DeepGCN-RT", "summary": "DeepGCN-RT predicts small-molecule retention times from molecular graphs.", "links": { "primary": "https://github.com/kangqiyue/DeepGCN-RT", "repository": "https://github.com/kangqiyue/DeepGCN-RT", "documentation": "https://github.com/kangqiyue/DeepGCN-RT#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "annotation_identification" ], "capabilities": [ "retention_time_prediction" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "Molecular SMILES and a trained model checkpoint" ], "other_output_formats": [ "Predicted retention times in seconds" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-08-19", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support. README distinguishes maintained pretrained inference from historical training code." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "DeepGCN-RT predicts small-molecule retention times from molecular graphs. The repository provides a pretrained inference command alongside research training and transfer-learning code associated with the published graph-convolutional model.", "scope": { "designed_for": [ "DeepGCN-RT predicts small-molecule retention times from molecular graphs." ], "not_designed_for": [ "Universal retention-time prediction across chromatographic methods without calibration or experimental validation." ], "requirements": [ "The documented Python 3.9/3.10 environment, valid SMILES and the released checkpoint; select a compatible CPU or CUDA device." ], "considerations": [ "Predictions reflect the training chromatographic domain. The README distinguishes maintained inference from historical training/transfer-learning scripts; the repository's smoke test is not an independent scientific benchmark." ] }, "common_uses": [ "DeepGCN-RT predicts small-molecule retention times from molecular graphs." ], "publications": [ { "doi": "10.1016/j.chroma.2023.464439", "type": "primary", "citation": "Deep graph convolutional network for small-molecule retention time prediction. Journal of Chromatography A (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "deepion", "name": "DeepION", "summary": "DeepION learns compact representations of MSI ion images for finding co-localized ions or candidate isotope relationships.", "links": { "primary": "https://github.com/BioNet-XMU/DeepION", "repository": "https://github.com/BioNet-XMU/DeepION", "documentation": "https://github.com/BioNet-XMU/DeepION#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "visualization_reporting" ], "capabilities": [ "multivariate_statistics" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "command_line" ], "access": { "model": "source_available", "notes": "Public research source; no repository-wide license grant was established from the reviewed metadata. Check dependency and data terms separately." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "imaging_data" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "Preprocessed MSI matrix, peak CSV, image dimensions and ion mode" ], "other_output_formats": [ "Learned image representations and co-localization/isotope search results" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-03-12", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "DeepION learns compact representations of MSI ion images for finding co-localized ions or candidate isotope relationships. Separate COL and ISO modes use augmented images and learned embeddings for downstream similarity analysis.", "scope": { "designed_for": [ "DeepION learns compact representations of MSI ion images for finding co-localized ions or candidate isotope relationships." ], "not_designed_for": [ "Direct raw-imzML processing, isotopologue abundance correction or proof that co-localized ions belong to the same molecule." ], "requirements": [ "The documented Python/PyTorch environment, a preprocessed pixel-by-ion matrix, peak list, image dimensions and ionization-mode settings." ], "considerations": [ "Image shape, preprocessing and missing pixels influence learned similarity. The documented legacy dependency combination needs compatibility checking before a new installation." ] }, "common_uses": [ "DeepION learns compact representations of MSI ion images for finding co-localized ions or candidate isotope relationships." ], "publications": [ { "doi": "10.1021/acs.analchem.3c05002", "type": "primary", "citation": "DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging. Analytical Chemistry (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "deeprtalign", "name": "DeepRTAlign", "summary": "Align retention times across large LC-MS sample collections.", "description": "DeepRTAlign uses a deep-learning-based workflow to match extracted LC-MS features across samples, with alignment scoring and quality-control output. The primary paper includes metabolomics as well as proteomics.", "links": { "primary": "https://github.com/PHOENIXcenter/deeprtalign", "repository": "https://github.com/PHOENIXcenter/deeprtalign", "documentation": "https://github.com/PHOENIXcenter/deeprtalign#readme" }, "platforms": [ "lc_ms" ], "interfaces": [ "command_line", "python_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "retention_time_alignment" ] }, "access": { "model": "open_source", "notes": "License checked against official repository/package metadata. Installation and execution were not tested.", "license": "GPL-3.0" }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Feature lists containing m/z, charge, RT and intensity; sample mapping workbook" ], "other_output_formats": [ "Aligned feature groups and scores in CSV files" ] }, "common_uses": [ "Align retention times across large LC-MS sample collections." ], "scope": { "designed_for": [ "DeepRTAlign uses a deep-learning-based workflow to match extracted LC-MS features across samples, with alignment scoring and quality-control output. The primary paper includes metabolomics as well as proteomics." ], "not_designed_for": [ "Metabolite identification or peak extraction directly from instrument raw files." ], "requirements": [ "Python, PyTorch and feature extraction completed upstream.", "Input column mappings and sample-file relationships prepared as documented." ], "considerations": [ "Match quality depends on chromatography, extraction and alignment parameters; confirm suitability for the study." ] }, "publications": [ { "doi": "10.1038/s41467-023-43909-5", "type": "primary", "citation": "DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis. Nature Communications (2023)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "deimos", "name": "DEIMoS", "summary": "An open-source Python and command-line toolkit for high-dimensional mass-spectrometry processing, including feature detection and alignment, CCS calibration, isotope detection, and MS/MS spectral deconvolution.", "links": { "primary": "https://deimos.readthedocs.io/", "homepage": "https://deimos.readthedocs.io/", "repository": "https://github.com/pnnl/deimos", "documentation": "https://deimos.readthedocs.io/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [ "spectral_analysis", "annotation_identification" ], "capabilities": [ "peak_detection", "retention_time_alignment", "spectral_deconvolution", "ms2_processing" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "BSD-3-Clause source distribution; consult the installation and API documentation when composing a workflow." }, "technical": { "operating_systems": [ "linux", "macos" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra", "feature_table" ], "output_types": [ "feature_table", "spectra" ], "input_formats": [ "mzml", "hdf5" ], "output_formats": [ "csv_tsv", "hdf5" ] }, "publications": [ { "doi": "10.1021/acs.analchem.1c05017", "type": "primary", "citation": "DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data. Analytical Chemistry (2022)." } ], "common_uses": [ "Process multidimensional MS datasets using multiple measured dimensions simultaneously.", "Detect and align features while retaining ion-mobility or other separation dimensions.", "Perform CCS calibration and MS/MS spectral deconvolution." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-21", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "High-dimensional mass-spectrometry data processing where dimensions such as retention time and ion mobility improve separation." ], "considerations": [ "DEIMoS is code-oriented and requires users to design or adapt a processing workflow around its API/CLI." ], "not_designed_for": [ "Predicting CCS from molecular structures or automatically identifying compounds solely from detected features." ], "requirements": [ "A Python environment with DEIMoS dependencies and data represented in the dimensions expected by the selected processing functions; calibration data are needed for CCS calibration." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia", "unspecified" ], "ion_mobility": "supported" }, "description": "DEIMoS provides an API and command-line components for multidimensional MS processing, including feature detection, alignment, isotope detection, CCS calibration and MS/MS deconvolution. Users compose these components into workflows appropriate to their acquisition dimensions." }, { "$schema": "../../schemas/tool.schema.json", "slug": "diffms", "name": "DiffMS", "summary": "DiffMS generates candidate molecular structures conditioned on mass-spectral information using a diffusion model.", "links": { "primary": "https://github.com/coleygroup/DiffMS", "repository": "https://github.com/coleygroup/DiffMS", "documentation": "https://github.com/coleygroup/DiffMS#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures" ], "other_input_formats": [ "Prepared spectra, molecular formula and model configuration/checkpoints" ], "other_output_formats": [ "Generated molecular candidates and training/evaluation artifacts" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "DiffMS generates candidate molecular structures conditioned on mass-spectral information using a diffusion model. The research pipeline combines fingerprint-to-molecule pretraining with end-to-end spectral fine-tuning and provides pretrained model resources.", "scope": { "designed_for": [ "DiffMS generates candidate molecular structures conditioned on mass-spectral information using a diffusion model." ], "not_designed_for": [ "Confirmed molecular identification or a turnkey raw-vendor-file analysis pipeline." ], "requirements": [ "The documented Python/PyTorch/RDKit environment, prepared dataset configuration and appropriate end-to-end or separate encoder/decoder checkpoints; published setup uses CUDA. The published model is restricted by a supplied molecular formula." ], "considerations": [ "Check data preparation, checkpoint compatibility and chemical-domain coverage. Preserve train/test separation and validate generated candidates with independent evidence." ] }, "common_uses": [ "DiffMS generates candidate molecular structures conditioned on mass-spectral information using a diffusion model." ], "publications": [ { "doi": "10.48550/arXiv.2502.09571", "citation": "Bohde et al. DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra. arXiv:2502.09571 (2025). Authors identify the associated conference as ICML 2025.", "type": "primary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "dreams", "name": "DreaMS", "summary": "A transformer-based framework for learning molecular representations from tandem mass spectra, supporting spectral similarity, clustering, molecular networking, and downstream prediction tasks.", "links": { "primary": "https://dreams-docs.readthedocs.io/", "homepage": "https://dreams-docs.readthedocs.io/", "repository": "https://github.com/pluskal-lab/DreaMS", "documentation": "https://dreams-docs.readthedocs.io/", "web_app": "https://huggingface.co/spaces/anton-bushuiev/DreaMS" }, "resource_types": [ "programming_package", "web_service" ], "functions": { "primary": "spectral_analysis", "secondary": [ "machine_learning", "molecular_networking", "annotation_identification" ], "capabilities": [ "library_matching", "ms2_processing" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "web_app", "notebook" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT-licensed code; pretrained weights and datasets are linked separately. The hosted demonstration is a separate execution route whose availability and submitted-data handling should be checked before use." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "statistical_results" ], "input_formats": [ "mgf", "hdf5" ], "other_output_formats": [ "Python embedding matrices and task-dependent similarity or prediction arrays" ] }, "publications": [ { "doi": "10.1038/s41587-025-02663-3", "type": "primary", "citation": "Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS. Nature Biotechnology (2025)." } ], "common_uses": [ "Generate learned embeddings for MS/MS spectra.", "Compare spectra using embedding similarity or build large-scale molecular networks.", "Fine-tune pretrained representations for task-specific prediction." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-06-05", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Representation learning and similarity analysis of small-molecule tandem mass spectra." ], "considerations": [ "Learned embedding similarity is a computational relationship and does not by itself establish molecular identity." ], "not_designed_for": [ "Raw chromatographic feature detection or definitive molecular identification from embedding similarity." ], "requirements": [ "The documented Python environment and pretrained model weights for local inference; MS/MS spectra with the required precursor metadata. Fine-tuning requires suitable labelled data and additional compute." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "description": "DreaMS generates learned vector representations of tandem mass spectra for similarity analysis and downstream prediction. The project provides pretrained models, tutorials and a library-matching web demonstration; local embeddings can be reused in clustering and molecular-network analysis." }, { "$schema": "../../schemas/tool.schema.json", "slug": "drug-readout", "name": "GNPS2 Drug Readout", "summary": "Annotate GNPS2 feature-based networking results with drug and drug-metabolite context for exposure-oriented interpretation.", "links": { "primary": "https://drugreadouts.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Drug_Readout/" }, "resource_types": [ "web_service" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "metadata_contextualization", "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly hosted MetaboApp. GNPS2 infrastructure is open to the academic community; its documentation directs industry users to the maintainers or UCR Technology Transfer. Hosted access does not establish a software or reference-data license." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against the linked official app documentation on 2026-09-10. No standalone publication for this particular app was verified; the documentation citation identifies the reviewed implementation. Underlying method and application papers are not presented as dedicated app papers. No analysis job or independent benchmark was run; no developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "feature_table", "compound_list" ], "output_types": [ "compound_list", "report" ], "other_input_formats": [ "Completed GNPS2 FBMN task ID; peak-area and blank-filter settings" ], "other_output_formats": [ "Annotation tables, class summaries and downloadable plot data" ] }, "scope": { "designed_for": [ "Research interpretation of drug-library matches in LC-MS/MS studies." ], "not_designed_for": [ "Establishing dose, adherence or exposure source from spectral similarity alone." ], "requirements": [ "A completed FBMN task with relevant library matches; documentation recommends the GNPS Drug Library.", "A suitable peak-area threshold; blank/control identifiers are optional." ], "considerations": [ "Separate reference matches from propagated analogues and inspect spectral evidence.", "Shared endogenous or food sources can complicate attribution to medication.", "Blank exclusion by column-name substring is not equivalent to validating every feature against procedural blanks." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Standalone MetaboApp that can be launched directly or from a GNPS2 task." }, { "slug": "gnps", "relationship": "integration", "note": "Consumes GNPS2 tasks or ecosystem data." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Uses results from a completed FBMN task." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "publications": [], "resource_citations": [ { "type": "documentation", "citation": "GNPS2 Documentation. GNPS2 Drug Readout. Accessed 10 September 2026.", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Drug_Readout/" } ], "description": "Drug Readout combines FBMN library annotations with drug metadata to summarize candidate drug-related signals across samples. It supports inspection of parent-drug and analogue matches and their pharmacological context.", "common_uses": [ "Inspect possible drug-related signals in untargeted datasets.", "Compare class summaries with or without analogue matches." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "eclipse", "name": "Eclipse", "summary": "Match corresponding features across untargeted LC-MS datasets.", "description": "Eclipse is a BMXP Python module that aligns feature tables using retention time, m/z and intensity, then aggregates matches through a graph.", "links": { "primary": "https://github.com/broadinstitute/bmxp", "repository": "https://github.com/broadinstitute/bmxp", "documentation": "https://github.com/broadinstitute/bmxp/blob/main/bmxp/eclipse/readme.md" }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "retention_time_alignment" ] }, "access": { "model": "source_available", "notes": "The LICENSE is headed MIT License (Dual License) but adds a commercial-redistribution restriction. Recorded as source available, not standard MIT; consult the actual license for intended use." }, "data": { "input_types": [ "feature_table" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "CSV or DataFrame tables with feature ID, RT, m/z and intensity" ], "other_output_formats": [ "Matched-feature CSV tables and alignment reports" ] }, "common_uses": [ "Match corresponding features across untargeted LC-MS datasets." ], "scope": { "designed_for": [ "Eclipse is a BMXP Python module that aligns feature tables using retention time, m/z and intensity, then aggregates matches through a graph." ], "not_designed_for": [ "Raw-file peak detection or structural confirmation of aligned features." ], "requirements": [ "Python with BMXP and extracted feature tables from compatible LC-MS methods." ], "considerations": [ "Inspect alignment diagnostics and thresholds. Different gradients require the documented prescaling; arbitrary methods are not automatically compatible." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btaf290", "type": "primary", "citation": "Eclipse : a Python package for alignment of two or more nontargeted LC-MS metabolomics datasets. Bioinformatics (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "everything-bagel", "name": "Everything Bagel", "summary": "Detect, align and gap-fill LC-MS features, with a GNPS2 workflow connecting raw LC-MS/MS data to feature-based molecular networking.", "platforms": [ "lc_ms" ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "GNPS2 homepage announcement, hosted workflow link, FBMN input definition and Crossref preprint abstract/metadata checked on 2026-09-23. Preprint full text was not accessible; detailed performance, format and standalone software claims remain unverified. No analysis job was run." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23", "last_editorial_review": "2026-09-23" }, "maintenance": { "status": "unclear", "last_checked": "2026-09-23" }, "aliases": [ "Everything Bagel Feature Finder", "Everything Bagel Workflow" ], "description": "Everything Bagel is a feature-finding resource for untargeted LC-MS analysis. Its 2026 preprint describes feature detection, retention-time alignment and gap filling. The GNPS2-hosted workflow combines feature finding with FBMN, making this an upstream processing route into subsequent network interpretation.", "links": { "primary": "https://gnps2.org/workflowinput?workflowname=everything_bagel_workflow", "documentation": "https://gnps2.org/homepage" }, "resource_types": [ "workflow_pipeline", "web_service" ], "interfaces": [ "web_app" ], "functions": { "primary": "preprocessing", "secondary": [ "molecular_networking" ], "capabilities": [ "peak_detection", "retention_time_alignment", "gap_filling" ] }, "access": { "model": "free_web_service", "notes": "Hosted GNPS2 execution requires an account and permits academic/non-commercial use; commercial access requires a license. A standalone code repository and software license were not verified in this review." }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "LC-MS/MS files accepted by the GNPS2 workflow form" ], "other_output_formats": [ "Aligned feature tables and GNPS2 FBMN results" ] }, "common_uses": [ "Generate aligned quantitative features from LC-MS data.", "Connect raw-data processing with GNPS2 feature-based molecular networking." ], "scope": { "designed_for": [ "Untargeted LC-MS feature finding and a raw-data-to-FBMN workflow." ], "not_designed_for": [ "A downstream statistical-analysis replacement or proof of compound identity." ], "requirements": [ "A GNPS2 account and compatible LC-MS(/MS) data; check the workflow form for file and parameter requirements." ], "considerations": [ "The dedicated publication is a preprint, not a peer-reviewed validation.", "Reported speed and benchmark performance are author claims and were not independently reproduced here.", "It is an upstream workflow, not solely an analysis of a previously generated network." ] }, "publications": [ { "doi": "10.64898/2026.08.17.744735", "type": "primary", "citation": "Shin Y, El Abiead Y, Jarmusch AK, et al. The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics. bioRxiv (2026), preprint.", "note": "Preprint; not peer reviewed." } ], "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Available as a hosted workflow in GNPS2." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "The GNPS2 workflow joins feature finding to FBMN; the FBMN input definition includes an Everything Bagel format option." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "fameta", "name": "FAMetA", "summary": "Estimate fatty-acid synthesis, import, elongation and desaturation from carbon-13 isotopologue profiles.", "description": "FAMetA is an R package for model-based fatty-acid metabolic analysis. The publication also describes a web application. Its workflow uses LipidMS for preprocessing and accucor for natural-abundance correction before fitting fatty-acid metabolic contributions.", "links": { "primary": "https://github.com/maialba3/FAMetA", "repository": "https://github.com/maialba3/FAMetA", "documentation": "https://github.com/maialba3/FAMetA" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "isotope_tracing_flux", "capabilities": [ "flux_estimation" ] }, "access": { "model": "open_source", "license": "GPL-2.0-or-later", "notes": "License declared as GPL (>= 2) in package DESCRIPTION. The paper describes a web app, but current hosted availability was not verified." }, "data": { "input_types": [ "isotope_tracing_data" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Fatty-acid carbon-13 mass-isotopologue profiles prepared for the R workflow" ], "other_output_formats": [ "Fitted fatty-acid metabolic contributions and graphical summaries" ] }, "common_uses": [ "Investigate fatty-acid metabolic contributions in tracer experiments." ], "scope": { "designed_for": [ "Model-based analysis of fatty-acid labeling." ], "not_designed_for": [ "Untargeted structure identification or unrestricted whole-network flux analysis." ], "requirements": [ "Suitable carbon-13 labeling data and the R/LipidMS/accucor dependencies." ], "considerations": [ "Model assumptions, tracer choice and isotopologue measurement quality constrain interpretation." ] }, "publications": [ { "doi": "10.1093/bib/bbad064", "type": "primary", "citation": "FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism. Briefings in Bioinformatics (2023)." } ], "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Discovered through the Enveda review intake; official repository and primary publication metadata checked on 2026-10-02. Editorial review only; software and performance not execution-tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "fasst", "name": "FASST", "summary": "Accelerated indexed infrastructure for searching very large collections of tandem mass spectra, enabling current GNPS2 repository-scale search services to return results at billion-spectrum scale.", "description": "FASST provides indexed tandem mass-spectral searching used by GNPS2 repository-search services. Its documented API supports querying indexed spectra and retrieving matches. Index coverage and service versions should be reported with any analysis.", "links": { "primary": "https://fasst.gnps2.org/fastsearch/", "web_app": "https://fasst.gnps2.org/fastsearch/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/api/" }, "resource_types": [ "web_service", "api_service" ], "functions": { "primary": "reference_data_search", "secondary": [ "spectral_analysis", "data_management_sharing" ], "capabilities": [ "repository_scale_search", "library_matching" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app", "api" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [], "common_uses": [ "Search indexed public MS/MS collections in seconds rather than scanning repositories serially.", "Provide the fast-search backend used by MASST and domain-specific MASST applications.", "Support programmatic repository-scale spectral search through GNPS2 services.", "Retrieve indexed spectral matches through the web interface or documented API for downstream metadata analysis." ], "scope": { "designed_for": [ "High-throughput exact/analog tandem mass-spectral searching across very large indexed collections." ], "requirements": [ "A centroided query MS/MS spectrum or compatible query identifier and appropriate similarity/tolerance settings." ], "considerations": [ "FASST is infrastructure as well as a user-facing search service; many researchers encounter it indirectly through MASST-derived applications.", "The content and versions of public indexes change over time.", "Report the selected index, search date, precursor and fragment tolerances, similarity threshold and whether analog mode was enabled." ], "not_designed_for": [ "Finding compounds that are absent from the selected spectral index, or proving absence from a sample." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "masst", "relationship": "uses" }, { "slug": "structuremasst", "relationship": "complementary" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed. Publication-scope audit on 8 September 2026: no dedicated FASST publication was confirmed. Cite the documented service and the method actually used; the MASST+/Networking+ paper is not attributed to FASST." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "aliases": [ "Fast Search Tool", "FASST Records", "FASSTrecords" ], "technical": { "operating_systems": [ "browser" ], "api_available": true }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra", "sample_metadata", "report" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "Universal Spectrum Identifier (USI) or JSON peak list" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "fastei", "name": "FastEI", "summary": "Search electron-ionization spectra against a large predicted reference library.", "description": "FastEI combines learned spectrum embeddings with approximate nearest-neighbor indexing for EI library matching. It provides a Windows GUI and Python example workflows.", "links": { "primary": "https://github.com/Qiong-Yang/FastEI", "repository": "https://github.com/Qiong-Yang/FastEI", "documentation": "https://github.com/Qiong-Yang/FastEI#readme" }, "platforms": [ "gc_ms" ], "interfaces": [ "desktop_gui", "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "library_matching" ] }, "access": { "model": "open_source", "notes": "Access classification follows official repository license metadata; code and software execution were not tested.", "license": "Apache-2.0" }, "data": { "input_types": [ "spectra", "spectral_library" ], "output_types": [ "compound_list" ], "other_input_formats": [ "EI peak lists; documented Python example accepts two-column CSV spectra" ], "other_output_formats": [ "Ranked library candidate matches" ] }, "common_uses": [ "Search electron-ionization spectra against a large predicted reference library." ], "scope": { "designed_for": [ "FastEI combines learned spectrum embeddings with approximate nearest-neighbor indexing for EI library matching. It provides a Windows GUI and Python example workflows." ], "not_designed_for": [ "General LC-ESI-MS/MS identification or raw chromatogram deconvolution." ], "requirements": [ "Compatible EI spectra plus the reference database, embedding model and index.", "Windows 64-bit for the packaged GUI, or the documented Python environment." ], "considerations": [ "Predicted-library matches remain candidates; database coverage and spectral quality limit interpretation." ] }, "publications": [ { "doi": "10.1038/s41467-023-39279-7", "type": "primary", "citation": "Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library. Nature Communications (2023)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "fbmn-stats", "name": "FBMN Stats", "summary": "Clean and statistically explore feature-based molecular networking abundance tables with guided notebooks and a web app.", "description": "FBMN-STATS provides an accompanying app and notebooks for downstream analysis of non-targeted LC-MS/MS feature tables. It connects data cleanup and statistical comparisons to the interpretation of molecular-network results. R and Python notebooks can be run locally; a QIIME2 route and hosted interfaces are documented. The GNPS2 downstream link calls this the Hitchhiker Statistics Guide.", "links": { "primary": "https://fbmn-statsguide.gnps2.org/", "documentation": "https://github.com/Functional-Metabolomics-Lab/FBMN-STATS", "repository": "https://github.com/Functional-Metabolomics-Lab/FBMN-STATS" }, "resource_types": [ "web_service", "workflow_pipeline" ], "functions": { "primary": "statistics", "capabilities": [ "blank_filtering", "batch_effect_correction", "univariate_statistics", "multivariate_statistics", "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "BSD-3-Clause source with hosted app and R/Python notebook routes. Cloud notebook use may require an account; local execution requires the documented dependencies. Hosted use is recommended by the maintainers for smaller datasets." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "FBMN quantification and metadata tables; layout depends on interface" ], "other_output_formats": [ "Statistical result files and notebook or app visualizations" ] }, "common_uses": [ "Examine group differences and multivariate patterns in processed LC-MS/MS data.", "Prepare statistical results for interpretation alongside molecular networks." ], "scope": { "designed_for": [ "Downstream quantitative analysis of feature-based molecular networking results." ], "not_designed_for": [ "Raw-file feature detection or definitive compound identification." ], "requirements": [ "Compatible feature tables and sample metadata, including grouping and blank information where applicable.", "Choose an interface and analysis appropriate to the study design." ], "considerations": [ "Legacy GNPS Quickstart exports may lack the reformatted outputs required by the current integration; maintainers recommend GNPS2.", "Normalization, missing values, batch structure and multiple comparisons require study-specific decisions.", "Notebooks and the web app need not expose identical operations; consult the selected interface documentation.", "The browser-only FBMN-STATS Guide at apps.gnps2.org is marked in development and is not interchangeable with the original notebooks and Streamlit app." ] }, "publications": [ { "citation": "Pakkir Shah AK, Walter A, Ottosson F, et al. Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data. Nature Protocols 20, 92–162 (2025; published online 2024).", "type": "methods", "doi": "10.1038/s41596-024-01046-3" } ], "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Listed in the GNPS2 MetaboApps catalogue." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Provides processed feature-abundance and molecular-network outputs for downstream analysis." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official repository, license, GNPS2 listing and direct methods publication reviewed on 2026-09-10. No analysis job or independent benchmark performed. Rechecked 2026-09-23: added documented Hitchhiker aliases and local notebook visibility without creating a duplicate." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-23", "last_verified": "2026-09-23", "last_editorial_review": "2026-09-23" }, "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "aliases": [ "FBMN-STATS", "FBmnStatsGUIde", "Hitchhiker's Guide to Statistical Analysis of Feature-based Molecular Networks", "Hitchhiker Statistics Guide" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "fiddle", "name": "FIDDLE", "summary": "FIDDLE predicts and rescores molecular-formula candidates from tandem mass spectra using deep learning.", "links": { "primary": "https://github.com/JosieHong/FIDDLE", "repository": "https://github.com/JosieHong/FIDDLE", "documentation": "https://msfiddle.readthedocs.io" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "molecular_formula_prediction" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "other_input_formats": [ "MGF spectra with required precursor and collision-energy metadata" ], "other_output_formats": [ "CSV ranked formula candidates and scores, plus optional evaluation" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-24", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "FIDDLE predicts and rescores molecular-formula candidates from tandem mass spectra using deep learning. Its command-line and Python workflows provide instrument-specific inference and ranked formula outputs, with research training/evaluation scripts separately available.", "scope": { "designed_for": [ "FIDDLE predicts and rescores molecular-formula candidates from tandem mass spectra using deep learning." ], "not_designed_for": [ "Full molecular-structure identification or guaranteed formula recovery outside the supported instrument and chemical domains." ], "requirements": [ "The inference package and downloaded models, plus MGF spectra containing the required title, precursor mass/type and collision-energy metadata; select the appropriate instrument model." ], "considerations": [ "Formula ranking and rescoring depend on spectral metadata and model selection. Optional BUDDY/SIRIUS comparisons add separate software and account requirements." ] }, "common_uses": [ "FIDDLE predicts and rescores molecular-formula candidates from tandem mass spectra using deep learning." ], "publications": [ { "doi": "10.1038/s41467-025-66060-9", "type": "primary", "citation": "FIDDLE: a deep learning method for chemical formulas prediction from tandem mass spectra. Nature Communications (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "fnicm", "name": "FNICM", "summary": "Prioritize core metabolite nodes from a perturbed metabolic network.", "description": "FNICM propagates seed-node scores through a directed network, builds a ranked subnetwork and identifies core nodes using controllability analysis.", "links": { "primary": "https://github.com/LiQi94/FNICM#readme", "repository": "https://github.com/LiQi94/FNICM", "documentation": "https://github.com/LiQi94/FNICM#readme" }, "platforms": [ "platform_independent" ], "interfaces": [ "desktop_gui" ], "resource_types": [ "software_application" ], "functions": { "primary": "pathway_interpretation", "capabilities": [ "feature_selection" ] }, "access": { "model": "unknown", "notes": "LICENSE and LICENSE.md state AFL-3.0, but the README restricts use to research and asks commercial users to contact the author. Resolve these conflicting terms before reuse; no unrestricted open-source claim is made." }, "data": { "input_types": [ "pathway_data", "compound_list" ], "output_types": [ "pathway_data", "report" ], "other_input_formats": [ "Directed edge-list TXT and initial node scores" ], "other_output_formats": [ "TrustRank scores, subnetwork edges/nodes and core-node lists" ] }, "common_uses": [ "Prioritize core metabolite nodes from a perturbed metabolic network." ], "scope": { "designed_for": [ "FNICM propagates seed-node scores through a directed network, builds a ranked subnetwork and identifies core nodes using controllability analysis." ], "not_designed_for": [ "Raw MS processing, molecular identification or demonstrated causal metabolite control." ], "requirements": [ "Windows executable; a directed global network and seed scores aligned to its nodes.", "Choose the number of top-ranked nodes used to form the subnetwork." ], "considerations": [ "Seed selection, network completeness and the chosen subnetwork size affect prioritization.", "Undetected nodes can be prioritized by propagation; this does not establish that they were measured in the experiment.", "Only a Windows executable was identified in the repository; it was not downloaded or run." ] }, "publications": [ { "doi": "10.1021/acs.analchem.3c04131", "type": "primary", "citation": "FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-23. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-23" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-23", "updated_at": "2026-10-23", "last_verified": "2026-10-23", "last_editorial_review": "2026-10-23" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "food-readout", "name": "GNPS2 Food Readout", "summary": "Annotate molecular features with dietary and food-ontology context to explore potential food-derived exposures.", "links": { "primary": "https://foodreadouts.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Food_Readout/" }, "resource_types": [ "web_service" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "metadata_contextualization", "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly hosted MetaboApp. GNPS2 infrastructure is open to the academic community; its documentation directs industry users to the maintainers or UCR Technology Transfer. Hosted access does not establish a software or reference-data license." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against the linked official app documentation on 2026-09-10. No standalone publication for this particular app was verified; the documentation citation identifies the reviewed implementation. Underlying method and application papers are not presented as dedicated app papers. No analysis job or independent benchmark was run; no developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "feature_table", "compound_list", "sample_metadata" ], "output_types": [ "compound_list", "report", "statistical_results" ], "input_formats": [ "csv_tsv" ], "other_input_formats": [ "Completed GNPS2 FBMN task ID" ], "other_output_formats": [ "Processed food tables and exportable box, PCA and volcano plots" ] }, "scope": { "designed_for": [ "Post-processing LC-MS/MS library matches in their food-related reference context." ], "not_designed_for": [ "Measuring absolute food intake or proving recent consumption." ], "requirements": [ "A completed FBMN task with GNPS library matches, a feature quantification table and sample metadata.", "CSV/TSV tables with matching feature and sample identifiers." ], "considerations": [ "Reference coverage is incomplete, and many compounds occur in food and non-food sources.", "Group differences require study-design and statistical review; an exploratory plot is not causal evidence." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Standalone MetaboApp that can be launched directly or from a GNPS2 task." }, { "slug": "gnps", "relationship": "integration", "note": "Consumes GNPS2 tasks or ecosystem data." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Uses results from a completed FBMN task." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "publications": [], "resource_citations": [ { "type": "documentation", "citation": "GNPS2 Documentation. GNPS2 Food Readout. Accessed 10 September 2026.", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Food_Readout/" } ], "description": "Food Readout joins GNPS library matches to food metadata and ontology categories. It offers feature-level and group-level views of candidate food-associated signals, including abundance plots and exploratory statistical displays.", "common_uses": [ "Explore food-associated annotations in nutrition or exposome studies.", "Inspect variation in candidate dietary markers across sample groups." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "foodb", "name": "FooDB", "summary": "Explore food constituents, chemical properties and reported dietary sources.", "description": "FooDB links compounds with foods and literature-derived information, helping researchers contextualize potential dietary metabolites.", "links": { "primary": "https://foodb.ca/", "documentation": "https://foodb.ca/" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "CC BY-NC 4.0; commercial reuse requires permission under the stated terms." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Food, compound or structure queries" ], "other_output_formats": [ "Food-composition and compound records" ] }, "publications": [], "common_uses": [ "Investigate possible food sources for a candidate metabolite.", "Cross-reference food compounds with other chemical databases." ], "scope": { "designed_for": [ "Investigate possible food sources for a candidate metabolite." ], "not_designed_for": [ "Assigning a unique dietary source from a compound match." ], "requirements": [ "A food, compound name or compatible chemical query." ], "considerations": [ "Food composition depends on processing and biological variation; inspect measurement sources before making exposure inferences." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed. No standalone publication was confirmed in this pass; use the official project citation guidance." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-07", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "foodmasst", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "foodmasst", "name": "foodMASST", "summary": "A domain-specific GNPS search resource for identifying foods and beverages whose reference metabolomics data contain spectra matching a query MS/MS spectrum.", "description": "foodMASST contextualizes known or unknown tandem mass spectra against a reference collection of foods and beverages assembled through public metabolomics data. Matching spectra are mapped to food/beverage items and associated metadata, enabling researchers to explore potential dietary origins and occurrence patterns of compounds in untargeted metabolomics.", "links": { "primary": "https://apps.gnps2.org/foodmasst", "web_app": "https://apps.gnps2.org/foodmasst", "repository": "https://github.com/robinschmid/microbe_masst", "documentation": "https://github.com/robinschmid/microbe_masst" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "secondary": [ "spectral_analysis", "data_management_sharing" ], "capabilities": [ "repository_scale_search", "metadata_contextualization", "library_matching" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling", "exposomics" ], "interfaces": [ "web_app", "python_package" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.1038/s41538-022-00137-3", "citation": "West KA, Schmid R, Gauglitz JM, et al. foodMASST a mass spectrometry search tool for foods and beverages. NPJ Sci Food 6, 22 (2022).", "type": "primary" } ], "common_uses": [ "Search a query MS/MS spectrum against curated food and beverage reference data.", "Generate hypotheses about dietary or food-associated origins of molecules detected in untargeted data.", "Explore exact or related spectral occurrences across food categories and associated metadata.", "Find food and beverage reference contexts for a spectrum observed in a biological sample." ], "scope": { "designed_for": [ "Food- and beverage-contextualization of tandem mass spectra." ], "requirements": [ "A query MS/MS spectrum or compatible spectrum identifier." ], "considerations": [ "A match in food reference data supports an occurrence hypothesis but does not by itself establish dietary exposure or provenance in a biological sample.", "Reference coverage depends on the foods and beverages deposited in the source collection.", "Inspect food labels and source datasets; shared ingredients and processing can produce matches across different food categories." ], "not_designed_for": [ "Estimating food intake or establishing that a dietary exposure caused a biological phenotype." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "fasst", "relationship": "uses" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser", "cross_platform" ], "programming_languages": [ "Python" ], "api_available": false }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra", "sample_metadata", "report" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "Universal Spectrum Identifier (USI) or peak list" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "fraghub", "name": "FragHub", "summary": "Standardize and combine reference mass-spectral libraries.", "description": "FragHub harmonizes spectral metadata and chemical identifiers, filters peak lists and organizes reference spectra for downstream library use.", "links": { "primary": "https://github.com/eMetaboHUB/FragHub", "repository": "https://github.com/eMetaboHUB/FragHub", "documentation": "https://github.com/eMetaboHUB/FragHub#readme" }, "platforms": [ "lc_ms", "gc_ms" ], "interfaces": [ "desktop_gui" ], "resource_types": [ "software_application" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "identifier_harmonization", "spectral_data_io" ] }, "access": { "model": "source_available", "notes": "Repository LICENSE states CC BY-NC 4.0, while the README badge names CeCILL. Treat licensing as inconsistent and confirm terms with maintainers before reuse; do not assume unrestricted open-source licensing.", "license": "CC-BY-NC-4.0" }, "data": { "input_types": [ "spectral_library" ], "output_types": [ "spectral_library" ], "other_input_formats": [ "MSP, MGF, JSON or documented CSV spectral-library files" ], "other_output_formats": [ "Standardized reference-library exports" ] }, "common_uses": [ "Standardize and combine reference mass-spectral libraries." ], "scope": { "designed_for": [ "FragHub harmonizes spectral metadata and chemical identifiers, filters peak lists and organizes reference spectra for downstream library use." ], "not_designed_for": [ "Raw chromatographic feature extraction or proof of compound identity." ], "requirements": [ "Reference libraries with the required spectrum and chemical metadata." ], "considerations": [ "Filtering can discard spectra with missing metadata; review exclusions.", "Source-library licenses still apply to combined or redistributed data." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c02219", "type": "primary", "citation": "FragHub: A Mass Spectral Library Data Integration Workflow. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "g-aligner", "name": "G-Aligner", "summary": "An open-source graph-based method for aligning untargeted LC-MS features across runs by solving the correspondence problem as a multidimensional assignment task.", "links": { "primary": "https://github.com/CSi-Studio/G-Aligner", "repository": "https://github.com/CSi-Studio/G-Aligner", "documentation": "https://github.com/CSi-Studio/G-Aligner#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [], "capabilities": [ "retention_time_alignment" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "MulanPSL-2.0", "notes": "The official README states MulanPSL-2.0. Installation includes separately installed solver components whose requirements should also be checked." }, "technical": { "operating_systems": [ "linux", "macos", "windows" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "feature_table" ], "output_types": [ "feature_table" ], "input_formats": [ "csv_tsv" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1186/s12859-023-05525-4", "type": "primary", "citation": "G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics. BMC Bioinformatics (2023)." } ], "common_uses": [ "Align extracted LC-MS features across multiple samples or runs.", "Replace or compare alignment stages from preprocessing workflows such as XCMS, OpenMS, or MZmine." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-03-09", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Cross-run feature alignment after feature extraction in untargeted LC-MS." ], "requirements": [ "Python environment, the supplied third-party solver components and per-run CSV feature lists with m/z, retention time and area columns; adjust the example configuration for the study." ], "considerations": [ "Poor upstream detection or inappropriate alignment tolerances can propagate into correspondence results; inspect missingness and ambiguous matches after alignment." ], "not_designed_for": [ "Raw-data peak detection or identification of the molecules represented by aligned features." ] }, "description": "G-Aligner aligns previously detected LC-MS features across runs using a multipartite-graph formulation of feature correspondence. It optimizes competing matches using m/z, retention time and abundance information rather than extracting peaks from raw files." }, { "$schema": "../../schemas/tool.schema.json", "slug": "gcmsformer", "name": "GCMSFormer", "summary": "Resolve overlapping GC-MS peaks into component spectra and profiles.", "description": "GCMSFormer combines transformer predictions with orthogonal-projection resolution and least-squares fitting to separate overlapping GC-MS signals.", "links": { "primary": "https://github.com/zxguocsu/GCMSFormer#readme", "repository": "https://github.com/zxguocsu/GCMSFormer", "documentation": "https://github.com/zxguocsu/GCMSFormer#readme" }, "platforms": [ "gc_ms" ], "interfaces": [ "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "capabilities": [ "spectral_deconvolution" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "MIT license verified in the official source repository. Training datasets and model downloads may have separate terms. No execution or independent performance benchmark." }, "data": { "input_types": [ "raw_instrument_data", "spectral_library" ], "output_types": [ "spectra", "feature_table" ], "other_input_formats": [ "GC-MS netCDF files plus trained model and reference vocabulary" ], "other_output_formats": [ "Resolved component spectra and chromatographic concentration profiles" ] }, "common_uses": [ "Resolve overlapping GC-MS peaks into component spectra and profiles." ], "scope": { "designed_for": [ "GCMSFormer combines transformer predictions with orthogonal-projection resolution and least-squares fitting to separate overlapping GC-MS signals." ], "not_designed_for": [ "LC-MS/MS processing or guaranteed identification of components absent from model/reference coverage." ], "requirements": [ "Python/PyTorch environment, trained model and matching reference vocabulary; see test.ipynb." ], "considerations": [ "Training uses augmented overlapping-peak simulations; validate separation on representative experimental mixtures.", "The provided reader works with netCDF; arbitrary vendor-native support is not established." ] }, "publications": [ { "doi": "10.1021/acs.analchem.3c05772", "type": "primary", "citation": "GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography–Mass Spectrometry. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "getfeatistics", "name": "GetFeatistics", "summary": "An R package for targeted and untargeted metabolomics feature-table processing, QC-aware analysis, and advanced statistical modeling including regression and mixed-effects models.", "links": { "primary": "https://github.com/FrigerioGianfranco/GetFeatistics", "repository": "https://github.com/FrigerioGianfranco/GetFeatistics", "documentation": "https://frigeriogianfranco.github.io/GetFeatistics/articles/GF_vignette.html" }, "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "secondary": [ "quality_control", "visualization_reporting" ], "capabilities": [ "univariate_statistics", "multivariate_statistics" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "R abundance tables plus sample legends and optional compound/calibration legends" ], "other_output_formats": [ "R concentration/statistical tables, model objects and ggplot figures" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-12", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "GetFeatistics elaborates already extracted targeted or untargeted metabolomics tables and supports QC, calibration and downstream statistical analysis. Its R functions include targeted concentration calculations and models such as linear or mixed-effects regression.", "scope": { "designed_for": [ "An R package for targeted and untargeted metabolomics feature-table processing, QC-aware analysis, and advanced statistical modeling including regression and mixed-effects models." ], "not_designed_for": [ "Extracting chromatographic features directly from raw MS files or choosing an appropriate statistical design automatically." ], "requirements": [ "R and architecture-matched Java dependencies; organize abundance tables with matching sample/feature metadata and provide calibration/internal-standard information for targeted quantification." ], "considerations": [ "Follow the separate targeted and untargeted table structures. Calibration, missing-value handling, covariates and repeated-measure assumptions must reflect the experiment." ] }, "common_uses": [ "An R package for targeted and untargeted metabolomics feature-table processing, QC-aware analysis, and advanced statistical modeling including regression and mixed-effects models." ], "publications": [ { "doi": "10.1515/jib-2025-0047", "type": "primary", "citation": "Streamlining feature elaboration and statistics analysis in metabolomics: the GetFeatistics R-package. Journal of Integrative Bioinformatics (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnn-rt", "name": "GNN-RT", "summary": "Predict chromatographic retention times from molecular structures.", "description": "GNN-RT learns molecular-graph representations for retention-time prediction, with training and transfer-learning scripts for applying the model to a chromatographic dataset.", "links": { "primary": "https://github.com/Qiong-Yang/GNN-RT", "repository": "https://github.com/Qiong-Yang/GNN-RT", "documentation": "https://github.com/Qiong-Yang/GNN-RT#readme" }, "platforms": [ "lc_ms" ], "interfaces": [ "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "capabilities": [ "retention_time_prediction" ] }, "access": { "model": "open_source", "notes": "Access classification follows official repository license metadata; code and software execution were not tested.", "license": "AGPL-3.0" }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "report" ], "other_input_formats": [ "Molecular structures and measured retention times for training/adaptation" ], "other_output_formats": [ "Model-predicted retention times" ] }, "common_uses": [ "Predict chromatographic retention times from molecular structures." ], "scope": { "designed_for": [ "GNN-RT learns molecular-graph representations for retention-time prediction, with training and transfer-learning scripts for applying the model to a chromatographic dataset." ], "not_designed_for": [ "Direct peak detection, structure confirmation or method-independent retention-time prediction." ], "requirements": [ "Python, PyTorch, RDKit and data prepared for the provided scripts.", "Training or transfer-learning data appropriate to the chromatographic method." ], "considerations": [ "Predictions depend on chemical and chromatographic applicability; use them as supporting annotation evidence." ] }, "publications": [ { "doi": "10.1021/acs.analchem.0c04071", "type": "primary", "citation": "Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification. Analytical Chemistry (2021)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-classical-molecular-networking", "name": "GNPS2 Classical Molecular Networking", "summary": "GNPS2 workflow for organizing MS/MS spectra into molecular families using pairwise spectral similarity and spectral-library searching.", "links": { "primary": "https://gnps2.org/workflowinput?workflowname=classic_networking", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/classical/" }, "resource_types": [ "workflow_pipeline", "web_service" ], "functions": { "primary": "molecular_networking", "capabilities": [ "library_matching", "network_visualization" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "GNPS2 is openly available to the academic community. The current GNPS2 documentation asks industry users to contact the maintainers or UC Riverside Technology Transfer regarding organizational access. Workflow submission requires a GNPS2 account." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against GNPS2 documentation and the linked method publication on 2026-09-09. No analysis job was submitted and no independent benchmarking or developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "data": { "input_types": [ "spectra" ], "output_types": [ "report", "compound_list" ], "input_formats": [ "mgf", "mzml", "mzxml" ], "other_output_formats": [ "Network edge and node tables, library matches and downloadable workflow results" ] }, "publications": [ { "citation": "Aron AT, Gentry EC, McPhail KL, et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nature Protocols 15, 1954–1991 (2020).", "type": "methods", "doi": "10.1038/s41596-020-0317-5", "note": "Describes classical GNPS molecular networking; the hosted GNPS2 implementation may differ from the published interface." } ], "common_uses": [ "Group related MS/MS spectra into molecular families.", "Search network nodes against public spectral libraries." ], "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Dedicated molecular-networking workflow hosted by GNPS2." } ], "maintenance": { "status": "active", "last_checked": "2026-09-09" }, "description": "Classical molecular networking organizes MS/MS spectra into a graph in which nodes represent consensus fragmentation spectra and edges connect spectra above defined similarity thresholds. The workflow is useful when chromatographic feature tables are unavailable or when the analysis is centered directly on MS/MS spectral relationships.", "scope": { "designed_for": [ "Discovering molecular families directly from untargeted MS/MS spectra without requiring a chromatographic feature table.", "Natural-product dereplication and exploratory organization of fragmentation-similar spectra." ], "requirements": [ "Centroided MS/MS data with precursor information and instrument-appropriate mass tolerances." ], "considerations": [ "Unlike FBMN, classical molecular networking is spectrum-centric and does not inherently preserve chromatographic feature quantification or resolve coeluting/isomeric features through an external feature table.", "Network topology depends on filtering and similarity thresholds; these parameters should be reported together with the GNPS task identifier." ], "not_designed_for": [ "Definitive structure identification from spectral similarity alone.", "Replacing experimental quality control or validating biological causation." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-cytoscape-bridge", "name": "GNPS2 Cytoscape Bridge", "summary": "Convert a GNPS2 GraphML network into a styled desktop Cytoscape session in the browser.", "description": "The bridge packages an existing GNPS2 network with node and edge tables and sample-group styling for desktop inspection. It does not calculate spectral similarity or build the original network.", "links": { "primary": "https://apps.gnps2.org/gnps2_cys_generator", "documentation": "https://apps.gnps2.org/gnps2_cys_generator" }, "resource_types": [ "web_service" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "network_visualization" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public browser interface. The documented conversion runs locally in the browser; task-ID input fetches data from GNPS2. No independent execution test or software-license verification performed." }, "data": { "input_types": [ "report" ], "output_types": [ "report" ], "other_input_formats": [ "GNPS2 network.graphml or supported task ID/status URL" ], "other_output_formats": [ "Cytoscape .cys session" ] }, "common_uses": [ "Move an existing GNPS2 network into desktop Cytoscape." ], "scope": { "designed_for": [ "GNPS2 network conversion with group styling and optional singletons." ], "not_designed_for": [ "Raw-data processing, network inference or validation of molecular identities." ], "requirements": [ "Compatible GraphML or an accessible supported GNPS2 task.", "Desktop Cytoscape to open the resulting session." ], "considerations": [ "Styling summarizes supplied groups; visual similarity is not chemical identification evidence.", "Session compatibility should be checked with the installed Cytoscape version." ] }, "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "GNPS2 browser application." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Accepts its GNPS2 network outputs." }, { "slug": "cytoscape", "relationship": "integration", "note": "Open generated .cys sessions in compatible desktop Cytoscape." } ], "publications": [], "resource_citations": [ { "type": "documentation", "citation": "GNPS2 Cytoscape .cys generator. Interface documentation. Accessed 10 September 2026.", "url": "https://apps.gnps2.org/gnps2_cys_generator" } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official interface documentation reviewed on 2026-09-10. No dedicated publication identified; general GNPS and Cytoscape papers are not presented as bridge publications. Cytoscape integration linked on 2026-09-12." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "maintenance": { "status": "active", "last_checked": "2026-09-10" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-dashboard", "name": "GNPS Dashboard", "summary": "A browser-based interactive viewer for raw and public mass-spectrometry data, chromatograms, spectra, quantitative traces, and GNPS-associated datasets without requiring local analysis software.", "description": "GNPS Dashboard provides interactive browser-based exploration of mass-spectrometry files and public repository data. It can render chromatographic and spectral views, inspect individual scans, generate TIC/XIC-style traces, support targeted/SRM-style visualization, and link directly from GNPS analyses and public datasets. It also helps users identify and share spectra through Universal Spectrum Identifiers and connect raw-data inspection with downstream GNPS2 workflows.", "links": { "primary": "https://dashboard.gnps2.org/", "web_app": "https://dashboard.gnps2.org/", "repository": "https://github.com/Wang-Bioinformatics-Lab/GNPS_LCMSDashboard", "documentation": "https://ccms-ucsd.github.io/GNPSDocumentation/lcms-dashboard/" }, "resource_types": [ "web_service" ], "functions": { "primary": "visualization_reporting", "secondary": [ "data_management_sharing", "preprocessing" ], "capabilities": [ "interactive_dashboard", "chromatogram_building", "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "targeted", "metabolite_profiling" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.1038/s41592-021-01339-5", "citation": "Petras D, Phelan VV, Acharya D, et al. GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser. Nat Methods 19, 134-136 (2022).", "type": "primary" } ], "common_uses": [ "Interactively inspect raw/public LC-MS and other supported MS files in a browser.", "Visualize chromatograms, extracted ion traces, spectra, and targeted transitions without installing local software.", "Navigate from public repository files or GNPS2 analyses to individual scans and shareable spectrum identifiers.", "Inspect chromatographic peaks and scan-level evidence underlying an annotation or unexpected sample signal." ], "scope": { "designed_for": [ "Rapid visual inspection and collaborative exploration of raw or public mass-spectrometry data." ], "requirements": [ "A supported public-file reference, repository dataset, or uploaded/accessible data source." ], "considerations": [ "Dashboard visualization complements rather than replaces comprehensive quantitative preprocessing and statistical workflows.", "Available plots and data access depend on file format and repository availability.", "Preserve a shareable view or file/scan identifier, and distinguish interactive visualization settings from analytical processing parameters." ], "not_designed_for": [ "Replacing a validated quantitative preprocessing pipeline or formal statistical analysis." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia", "prm_mrm", "unspecified" ], "ion_mobility": "supported" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "massive", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser" ], "api_available": false }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "spectra", "report" ], "input_formats": [ "mzml", "mzxml" ], "output_formats": [ "json" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-feature-based-molecular-networking", "name": "GNPS2 Feature-Based Molecular Networking workflow", "summary": "Dedicated GNPS2 workflow that combines an aligned LC-MS feature table with MS/MS spectra to build quantitative molecular networks.", "links": { "primary": "https://gnps2.org/workflowinput?workflowname=feature_based_molecular_networking_workflow", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/fbmn/" }, "resource_types": [ "workflow_pipeline", "web_service" ], "functions": { "primary": "molecular_networking", "capabilities": [ "library_matching", "network_visualization" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "GNPS2 is openly available to the academic community. The current GNPS2 documentation asks industry users to contact the maintainers or UC Riverside Technology Transfer regarding organizational access. Workflow submission requires a GNPS2 account." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against GNPS2 documentation and the linked method publication on 2026-09-09. No analysis job was submitted and no independent benchmarking or developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-09", "last_verified": "2026-09-09", "last_editorial_review": "2026-09-09" }, "data": { "input_types": [ "feature_table", "spectra", "sample_metadata" ], "output_types": [ "feature_table", "compound_list", "report" ], "input_formats": [ "csv_tsv", "mgf", "msp", "mzml" ], "other_output_formats": [ "Network edge and node tables, library matches and downloadable workflow results" ] }, "publications": [ { "citation": "Nothias L-F, Petras D, Schmid R, et al. Feature-based molecular networking in the GNPS analysis environment. Nature Methods 17, 905–908 (2020).", "type": "primary", "doi": "10.1038/s41592-020-0933-6" } ], "common_uses": [ "Build molecular networks that retain chromatographic feature abundance across samples.", "Layer metadata and quantitative information onto MS/MS molecular families." ], "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Dedicated GNPS2 workflow." }, { "slug": "mzmine", "relationship": "integration", "note": "MZmine can export feature tables and spectra for FBMN." }, { "slug": "ms-dial", "relationship": "integration", "note": "MS-DIAL can provide compatible feature/spectral exports." } ], "maintenance": { "status": "active", "last_checked": "2026-09-09" }, "description": "Feature-Based Molecular Networking bridges upstream LC-MS/MS feature-processing tools with GNPS molecular networking. A quantitative feature table and representative MS/MS spectra are exported from software such as MZmine, MS-DIAL, XCMS, or OpenMS and used to build molecular networks in which each node corresponds to an aligned chromatographic feature.", "scope": { "designed_for": [ "Untargeted LC-MS/MS studies where chromatographic feature detection and quantitative feature tables are important for molecular-network interpretation.", "Resolving isomeric or chromatographically separated features that would otherwise be combined in purely spectrum-centric networking." ], "requirements": [ "An aligned feature quantification table and representative MS/MS spectra exported from a compatible preprocessing workflow.", "Matching feature identifiers between the quantification table and spectral summary; sample metadata is optional." ], "considerations": [ "The quality of the molecular network depends on upstream feature detection, alignment, MS/MS assignment, and preprocessing choices.", "FBMN does not remove the need for independent structural validation; library matches and network propagation represent different levels of annotation evidence.", "Raw mzML files are optional supporting inputs in the documented GNPS2 workflow; they do not replace the required processed feature table and spectral summary." ], "not_designed_for": [ "Definitive structure identification from spectral similarity alone.", "Replacing experimental quality control or validating biological causation." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-massql-analysis", "name": "GNPS2 MassQL Analysis", "summary": "Hosted interface for exploring MS/MS spectral libraries with MassQL and using LLM assistance to draft or explain queries.", "description": "The GNPS2 MassQL Analysis app combines spectral-library query exploration with assisted query construction. It is distinct from the local-file MassQL Playground and from the underlying MassQL language.", "links": { "primary": "https://massql-analysis.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/toolindex/#massql-analysis-app" }, "resource_types": [ "web_service" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "ms2_processing" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly listed hosted GNPS2 interface. Availability, account requirements and LLM-service limits should be checked in the app; no software or data license is inferred from hosted access." }, "data": { "input_types": [ "spectra" ], "other_input_formats": [ "MassQL query text and spectral-library selections through the hosted interface" ], "output_types": [ "report" ], "other_output_formats": [ "Interactive query results and query explanations; export formats not verified" ] }, "common_uses": [ "Explore fragmentation queries against MS/MS spectral libraries.", "Draft or explain a MassQL query with the app's LLM assistance." ], "scope": { "designed_for": [ "Interactive library-query exploration and learning MassQL syntax." ], "not_designed_for": [ "Treating generated query text or a library hit as a validated identification." ], "requirements": [ "Internet access and the hosted app; check requirements for assisted features." ], "considerations": [ "Inspect generated queries, tolerances and selected libraries before interpreting matches.", "The reviewed documentation does not establish model accuracy or guarantee export compatibility.", "Do not assume the hosted app shares the local-file privacy properties of MassQL Playground." ] }, "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Hosted tool listed in the official GNPS2 tool index." }, { "slug": "massql", "relationship": "integration", "note": "Interface to MassQL query construction and execution." }, { "slug": "massql-playground", "relationship": "alternative", "note": "Browser-only query development against supported local files." } ], "publications": [], "resource_citations": [ { "citation": "GNPS2 Documentation. MassQL Analysis App. Accessed 11 September 2026.", "type": "documentation", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/toolindex/#massql-analysis-app" } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official GNPS2 tool index and reachable app shell reviewed on 2026-09-11. No standalone publication for this interface verified. Query execution, account requirements, LLM quality and export formats were not independently tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-11", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-network-overlay", "name": "GNPS2 Network Overlay", "summary": "Attach custom feature attributes to an existing GNPS2 molecular network using matching node identifiers.", "description": "The network-overlay workflow joins an uploaded feature table to a completed network. The documented Progenesis route launches it from FBMN and supplies the existing spectra and graph automatically. The overlay adds supplied attributes; it does not independently establish their biological meaning.", "links": { "primary": "https://gnps2.org/workflowinput?workflowname=gnps2_network_overlay_workflow", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/protocol_fbmn_progenesis_overlay/" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "network_visualization" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted GNPS2 resource. Check hosted-service terms and account requirements; a software license is not inferred from hosting." }, "publications": [], "resource_citations": [ { "type": "documentation", "citation": "GNPS2 Network Overlay official documentation. Accessed 24 September 2026.", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/protocol_fbmn_progenesis_overlay/" } ], "maintenance": { "status": "unclear", "last_checked": "2026-09-24" }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation reviewed on 2026-09-24. No dedicated publication verified; no analysis execution or independent benchmark claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-24", "updated_at": "2026-09-24", "last_verified": "2026-09-24", "last_editorial_review": "2026-09-24" }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "report" ], "other_input_formats": [ "GraphML network, MGF spectra and an overlay table with a node-ID column" ], "other_output_formats": [ "GNPS2 network results with supplied overlay attributes" ] }, "common_uses": [ "Add feature-level information to a completed molecular network." ], "scope": { "designed_for": [ "Overlaying table attributes on existing GNPS2 network nodes." ], "not_designed_for": [ "Generating new measured evidence or validating uploaded annotations." ], "requirements": [ "A completed network, its spectra and a compatible overlay table.", "A node-ID column that matches network identifiers; follow the documented column-header restrictions." ], "considerations": [ "Mismatched identifiers can prevent correct attribute assignment.", "The linked tutorial demonstrates Progenesis/FBMN; no broader format support was independently tested.", "The source repository linked from the workflow collection could not be retrieved during review." ] }, "related_tools": [ { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Documented launch from completed FBMN results." }, { "slug": "gnps-classical-molecular-networking", "relationship": "integration", "note": "Also linked from the official classical-networking result definition." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-reverse-metabolomics", "name": "GNPS2 Reverse Metabolomics", "summary": "Search public MS/MS data using spectrum identifiers and explore matched samples by their biological metadata.", "links": { "primary": "https://reverse-metabolomics.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Reverse_Metabolomics/" }, "resource_types": [ "web_service" ], "functions": { "primary": "reference_data_search", "capabilities": [ "metadata_contextualization" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly hosted MetaboApp. GNPS2 infrastructure is open to the academic community; its documentation directs industry users to the maintainers or UCR Technology Transfer. Hosted access does not establish a software or reference-data license." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against the linked official app documentation on 2026-09-10. No standalone publication for this particular app was verified; the documentation citation identifies the reviewed implementation. Underlying method and application papers are not presented as dedicated app papers. No analysis job or independent benchmark was run; no developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "spectra" ], "output_types": [ "sample_metadata", "report" ], "other_input_formats": [ "Universal Spectrum Identifiers (USIs) for MS/MS spectra" ], "output_formats": [ "csv_tsv" ], "other_output_formats": [ "PNG heatmaps" ] }, "scope": { "designed_for": [ "Repository-scale context searching from annotated or unannotated MS/MS spectra." ], "not_designed_for": [ "Direct structure-string searching or proof of biological production." ], "requirements": [ "Resolvable MS/MS USIs and suitable MASST search parameters." ], "considerations": [ "Matches reflect spectral similarity and available metadata, not confirmed identity or origin.", "Repository sampling is uneven; absence of a match does not establish biological absence.", "ReDU-normalized counts adjust for reference sampling but do not remove every source of bias." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Standalone MetaboApp that can be launched directly or from a GNPS2 task." }, { "slug": "gnps", "relationship": "integration", "note": "Consumes GNPS2 tasks or ecosystem data." }, { "slug": "masst", "relationship": "integration", "note": "Retrieves spectral matches." }, { "slug": "redu", "relationship": "integration", "note": "Provides metadata context and normalization reference." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "publications": [], "resource_citations": [ { "citation": "GNPS2 Documentation. GNPS2 Reverse Metabolomics. Accessed 10 September 2026.", "type": "documentation", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Reverse_Metabolomics/" } ], "description": "GNPS2 Reverse Metabolomics links MASST spectral matches to public sample metadata. It helps investigate the recorded contexts of a spectrum through heatmaps and tables. This Tool is the hosted implementation; the separate Reverse metabolomics Strategy describes the broader experimental approach.", "common_uses": [ "Explore organisms, tissues or biofluids associated with spectral matches.", "Compare match distributions across metadata categories." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-spectrum-summary", "name": "GNPS2 MS/MS Spectrum Summary", "summary": "Explore recurring fragment peaks and neutral losses across selected tandem mass spectra.", "description": "The spectrum-summary app displays peak and neutral-loss histograms for spectra selected through a GNPS2 network component or a list of Universal Spectrum Identifiers. It supports inspection of shared fragmentation evidence before more detailed annotation.", "links": { "primary": "https://spectrasummary.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/toolindex/#msms-spectrum-summary-app" }, "resource_types": [ "web_service" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public hosted interface; input spectra must be retrievable through the selected GNPS task or USI route. No source-code license or independent performance benchmark was verified." }, "data": { "input_types": [ "spectra" ], "output_types": [ "report" ], "other_input_formats": [ "Universal Spectrum Identifiers, one per line", "GNPS task and network-component identifiers with node selection" ], "other_output_formats": [ "Interactive peak and neutral-loss histograms" ] }, "common_uses": [ "Inspect common fragment signals among related spectra.", "Examine recurring neutral losses in a selected network component." ], "scope": { "designed_for": [ "Exploratory comparison of shared fragmentation across selected MS/MS spectra." ], "not_designed_for": [ "Definitive structure identification or quantitative feature-table processing." ], "requirements": [ "Accessible spectra referenced by USIs or a supported GNPS network task.", "Selection of spectra appropriate to the scientific comparison." ], "considerations": [ "Shared peaks and losses support hypotheses but are not unique structural assignments.", "Acquisition conditions, precursor selection and the chosen spectrum subset affect the summary.", "Input controls and plots were inspected without submitting a user dataset; operational performance remains untested." ] }, "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Listed in the official GNPS2 tool index." } ], "publications": [], "resource_citations": [ { "type": "documentation", "citation": "GNPS2 Tool Index. MS/MS Spectrum Summary App. Accessed 10 September 2026.", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/toolindex/#msms-spectrum-summary-app" } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official tool index and both public input screens reviewed on 2026-09-10. No dedicated publication identified; no general GNPS paper added as an app-specific publication. No private data uploaded or independent benchmark performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "maintenance": { "status": "active", "last_checked": "2026-09-10" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps-usi-playground", "name": "GNPS2 USI Playground", "summary": "Prepare spectrum references from GNPS2 jobs, public dataset identifiers and MassIVE file paths.", "description": "The USI Playground provides separate forms for dataset identifiers, feature-based molecular networking tasks and MassIVE paths. These helpers support assembling references for downstream spectrum access; they do not establish metabolite identities.", "links": { "primary": "https://usi-playground.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/usi/" }, "resource_types": [ "web_service" ], "functions": { "primary": "data_management_sharing", "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public interface using accessible GNPS2 or MassIVE resources. No software-license or performance assessment was performed." }, "data": { "input_types": [ "report" ], "output_types": [ "report" ], "other_input_formats": [ "Dataset identifiers, GNPS2 task identifiers or MassIVE file paths" ], "other_output_formats": [ "Identifier tables and FBMN results with USIs" ] }, "common_uses": [ "Prepare references for retrieving spectra in downstream tools.", "Link GNPS2 feature-based molecular networking results to spectrum identifiers." ], "scope": { "designed_for": [ "Identifier preparation for public MS data and GNPS2 analysis results." ], "not_designed_for": [ "Raw-data processing, annotation or long-term data deposition." ], "requirements": [ "Valid accessible dataset, task or file-path references for the selected form." ], "considerations": [ "A reference depends on the availability and structure of the underlying data.", "Correct identifier formatting does not establish spectrum quality or molecular identity.", "The FBMN form displayed a Streamlit deprecation warning during review; verify downloaded results before downstream use." ] }, "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "GNPS2 identifier helper listed in the official tool index." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Dedicated form accepts a GNPS2 FBMN task identifier." }, { "slug": "massive", "relationship": "integration", "note": "Includes forms for public datasets and MassIVE file paths." } ], "publications": [], "resource_citations": [ { "type": "documentation", "citation": "GNPS2 Documentation. Universal Spectrum Identifiers. Accessed 10 September 2026.", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/usi/" } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official GNPS2 documentation and all three public forms inspected on 2026-09-10. No dedicated app publication identified. No user data submitted or independent end-to-end analysis performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "maintenance": { "status": "active", "last_checked": "2026-09-10" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "gnps", "name": "GNPS", "summary": "A web-based mass-spectrometry ecosystem for molecular networking, spectral-library search, repository-scale search, interactive visualization, knowledgebase curation, and public-data integration.", "links": { "primary": "https://gnps2.org/", "web_app": "https://gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/", "repository": "https://github.com/CCMS-UCSD/GNPS_Workflows" }, "resource_types": [ "web_service", "spectral_library", "database_knowledgebase", "workflow_platform" ], "functions": { "primary": "molecular_networking", "secondary": [ "annotation_identification", "spectral_analysis", "reference_data_search", "data_management_sharing", "workflow_reproducibility" ], "capabilities": [ "interactive_dashboard", "library_matching", "metadata_contextualization", "network_visualization", "provenance_tracking", "repository_scale_search" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "metabolite_profiling", "lipidomics" ], "interfaces": [ "web_app", "api", "workflow_language" ], "access": { "model": "free_web_service", "notes": "GNPS2 is openly available to the academic community. The current GNPS2 documentation asks industry users to contact the maintainers or UC Riverside Technology Transfer regarding organizational access." }, "technical": { "operating_systems": [ "browser" ], "api_available": true }, "data": { "input_types": [ "spectra", "feature_table", "sample_metadata" ], "output_types": [ "spectra", "compound_list", "report" ], "input_formats": [ "mzml", "mzxml", "mgf", "msp", "csv_tsv" ], "output_formats": [ "csv_tsv", "mgf", "msp", "json" ] }, "publications": [ { "doi": "10.1038/nbt.3597", "citation": "Wang M, Carver JJ, Phelan VV, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 34, 828-837 (2016).", "type": "primary" }, { "doi": "10.1038/s41596-020-0317-5", "citation": "Aron AT, Gentry EC, McPhail KL, et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc 15, 1954-1991 (2020).", "type": "methods", "note": "Describes a reproducible workflow or method implemented within GNPS." }, { "doi": "10.1038/s41592-020-0933-6", "citation": "Nothias LF, Petras D, Schmid R, et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods 17, 905-908 (2020).", "type": "methods", "note": "Describes a reproducible workflow or method implemented within GNPS." }, { "doi": "10.1038/s41467-021-23953-9", "citation": "Schmid R, Petras D, Nothias LF, et al. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat Commun 12, 3832 (2021).", "type": "methods", "note": "Describes a reproducible workflow or method implemented within GNPS." } ], "common_uses": [ "Search experimental MS/MS spectra against community and reference spectral libraries.", "Build classical, feature-based, and ion-identity molecular networks for untargeted MS/MS datasets.", "Search public metabolomics repositories using MASST, FASST-based services, and domain-specific MASST resources.", "Visualize raw data and analysis results, harmonize metadata, curate knowledge resources, and launch downstream GNPS2/MetaboApps analyses.", "Connect feature abundances to spectral similarity networks and inspect library annotations in experimental context." ], "related_tools": [ { "slug": "mzmine", "relationship": "integration" }, { "slug": "ms-dial", "relationship": "integration" }, { "slug": "massql", "relationship": "integration" }, { "slug": "masst", "relationship": "contains", "note": "MASST is a repository-scale spectral search service within the GNPS ecosystem." }, { "slug": "msms-chooser", "relationship": "contains", "note": "MSMS-Chooser is a GNPS workflow for selecting reference MS/MS spectra." }, { "slug": "fasst", "relationship": "contains", "note": "FASST provides the accelerated spectral-search infrastructure used by current GNPS2 repository-search services." }, { "slug": "structuremasst", "relationship": "contains", "note": "StructureMASST is a structure-centric pan-repository search service in the GNPS2 ecosystem." }, { "slug": "microbemasst", "relationship": "contains", "note": "microbeMASST contextualizes spectra against curated microbial monoculture data." }, { "slug": "plantmasst", "relationship": "contains", "note": "plantMASST contextualizes spectra against taxonomically defined plant data." }, { "slug": "foodmasst", "relationship": "contains", "note": "foodMASST contextualizes spectra against food and beverage reference data." }, { "slug": "microbiomemasst", "relationship": "contains", "note": "microbiomeMASST maps spectral matches across harmonized microbiome study metadata." }, { "slug": "modifinder", "relationship": "contains", "note": "ModiFinder localizes structural modification sites from paired MS/MS spectra." }, { "slug": "gnps-dashboard", "relationship": "contains", "note": "GNPS Dashboard provides interactive visualization and exploration of raw/public MS data." }, { "slug": "cmmc-kb", "relationship": "contains", "note": "CMMC-KB is a community-curated microbial metabolite knowledge resource in the GNPS2 ecosystem." }, { "slug": "metaboapps", "relationship": "contains", "note": "MetaboApps is a modular collection of downstream GNPS2 analysis applications." } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "MS/MS-centered annotation, molecular networking, public-data search, visualization, and community sharing of metabolomics and natural-products data.", "Web-based workflows that integrate processed feature tables, spectra, sample metadata, and public mass-spectrometry datasets." ], "requirements": [ "Inputs depend on the selected workflow; molecular-networking workflows generally require centroided MS/MS data, while FBMN also requires a feature table and representative MS/MS spectra.", "Appropriate metadata, feature-processing parameters, and workflow settings should be recorded for reproducible analysis." ], "considerations": [ "GNPS is an ecosystem rather than a single algorithm; publications should report the specific workflow, parameters, and task URL or identifier used.", "GNPS2 is actively evolving, and workflow interfaces or recommended settings can change; consult the current documentation when reproducing older analyses.", "Not every GNPS2 web utility is represented as a separate Navigator entry; dedicated entries are reserved for stable, reusable resources with distinct scientific functions.", "Preserve the task identifier, input feature IDs, metadata and library information so annotations can be traced to the original data." ], "not_designed_for": [ "Establishing a confirmed compound identity solely from a network edge or spectral-library score." ] }, "acquisition": { "ms_levels": [ "ms2", "msn" ], "strategies": [ "dda", "dia", "targeted_msms", "unspecified" ], "ion_mobility": "supported" }, "description": "GNPS/GNPS2 is a web-based ecosystem for analysis, annotation, visualization, curation, and reuse of tandem mass-spectrometry data. GNPS2 organizes its current infrastructure around computational workflows, interactive visualization, mass-spectrometry knowledgebase curation, and large-scale data integration. The ecosystem includes molecular networking, library searching, MASST/FASST-based public-data search, domain-specific MASST services, MassQL, Pan-ReDU, GNPS Dashboard, CMMC resources, MetaboApps, and interoperable workflows with external processing software.", "aliases": [ "Global Natural Products Social Molecular Networking", "GNPS2" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "goslin", "name": "Goslin", "functions": { "primary": "data_management_sharing", "capabilities": [ "identifier_harmonization" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "lipidomics" ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official repository and documentation reviewed on 2026-09-11. Included for downstream MS lipidomics. No installation, parser execution or user-data upload performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-11", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "data": { "input_types": [ "compound_list" ], "output_types": [ "compound_list" ], "other_input_formats": [ "Lipid shorthand names" ], "other_output_formats": [ "Standardized lipid identifiers" ] }, "summary": "Lipid shorthand grammars and parser implementations for validating and normalizing names in MS lipidomics workflows.", "description": "Goslin defines grammars for lipid shorthand notation, with implementations in several programming languages and a web/API project. Goslin 2.0 incorporates updated structural levels. This canonical entry covers the grammar ecosystem rather than treating each implementation as a separate analytical tool.", "links": { "primary": "https://lifs-tools.github.io/goslin/", "documentation": "https://lifs-tools.github.io/goslin/", "repository": "https://github.com/lifs-tools/goslin" }, "resource_types": [ "programming_package" ], "interfaces": [ "python_package", "r_package", "api" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT applies to the grammar repository. Check the license, installation requirements and supported interfaces of the selected language implementation separately." }, "common_uses": [ "Validate lipid shorthand before integrating result tables.", "Normalize supported lipid names within scripted analysis pipelines." ], "scope": { "designed_for": [ "Grammar-based parsing of supported lipid nomenclatures." ], "not_designed_for": [ "Identification from spectra or confirmation of double-bond positions." ], "requirements": [ "Lipid name strings and a suitable Goslin implementation." ], "considerations": [ "Record the parser version and inspect rejected names.", "Syntactic validity does not establish experimental identification confidence." ] }, "publications": [ { "citation": "Kopczynski D, Hoffmann N, Peng B, Ahrends R. Goslin: A Grammar of Succinct Lipid Nomenclature. Analytical Chemistry 92, 10957–10960 (2020).", "doi": "10.1021/acs.analchem.0c01690", "type": "primary" }, { "citation": "Kopczynski D, et al. Goslin 2.0 Implements the Recent Lipid Shorthand Nomenclature for MS-Derived Lipid Structures. Analytical Chemistry 94, 6097–6101 (2022).", "doi": "10.1021/acs.analchem.1c05430", "type": "methods" } ], "related_tools": [ { "slug": "lipidlynxx", "relationship": "alternative", "note": "Provides nomenclature conversion and resource linking; supported notation and outputs differ." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "graphia", "name": "Graphia", "summary": "Graph exploration and correlation-network analysis for processed MS metabolomics tables and existing networks.", "links": { "primary": "https://graphia.app/", "repository": "https://github.com/graphia-app/graphia", "documentation": "https://graphia.app/userguide.html" }, "resource_types": [ "software_application" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "network_visualization", "interactive_dashboard" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "Open-source desktop software with Windows, macOS and Linux downloads. The official site also offers a browser version; assess its suitability separately for your data and deployment requirements." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official project documentation and direct tool publications reviewed on 2026-09-12. Included for downstream MS metabolomics. No installation, dataset analysis or independent performance benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "data": { "input_types": [ "feature_table", "statistical_results", "pathway_data" ], "output_types": [ "report" ], "other_output_formats": [ "Interactive network views and exported analysis results" ], "other_input_formats": [ "CSV numeric tables or GraphML graphs" ] }, "publications": [ { "citation": "Freeman TC, et al. Graphia: A platform for the graph-based visualisation and analysis of high dimensional data. PLOS Computational Biology 18, e1010310 (2022).", "type": "primary", "doi": "10.1371/journal.pcbi.1010310" } ], "related_tools": [ { "slug": "cytoscape", "relationship": "alternative", "note": "Useful when very large graphs or 3D graph exploration are central." } ], "description": "Graphia imports graphs or builds correlation networks from numerical tables, with clustering and interactive 2D or 3D views. It is a general visual-analysis environment included here for downstream MS metabolomics, rather than a mass-spectrometry processing engine.", "common_uses": [ "Explore feature-correlation networks after preprocessing.", "Cluster and compare annotated graph components." ], "scope": { "designed_for": [ "General graph analysis and visual exploration of numerical-data relationships." ], "not_designed_for": [ "MS/MS matching, raw-data processing or inference of causal biochemical interactions." ], "requirements": [ "A cleaned numerical matrix or an existing graph with meaningful identifiers." ], "considerations": [ "Normalization, missing values, batch effects and correlation thresholds influence network structure.", "Graph clusters are exploratory patterns and need independent biological assessment." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "hmdb", "name": "HMDB", "summary": "Find chemical, biological and spectral reference information for human metabolites.", "description": "The Human Metabolome Database links compound records to biological context and experimental or predicted analytical information. Evidence types vary across entries.", "links": { "primary": "https://hmdb.ca/", "documentation": "https://hmdb.ca/" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly accessible database; check HMDB download and commercial-use terms before bulk reuse." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Names, identifiers, structures or spectral queries" ], "other_output_formats": [ "Metabolite records and selected downloads" ] }, "publications": [ { "citation": "Wishart DS, Guo A, Oler E, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res 50, D622-D631 (2022).", "type": "primary", "doi": "10.1093/nar/gkab1062" } ], "common_uses": [ "Look up synonyms and structures for human metabolites.", "Review available spectral and biological evidence for an annotation." ], "scope": { "designed_for": [ "Look up synonyms and structures for human metabolites." ], "not_designed_for": [ "Assuming every listed compound has been experimentally measured in the study population." ], "requirements": [ "A compound name, identifier, structure or compatible analytical query." ], "considerations": [ "Distinguish measured information from predictions and expected metabolites; inspect the source supporting each field." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "pubchem", "relationship": "complementary" }, { "slug": "chebi", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "hruv", "name": "hRUV", "summary": "An R package for hierarchical normalization of multi-batch metabolomics studies using intra-batch and inter-batch replicate information to estimate and remove unwanted variation.", "links": { "primary": "https://github.com/SydneyBioX/hRUV", "repository": "https://github.com/SydneyBioX/hRUV", "documentation": "https://sydneybiox.github.io/hRUV/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "normalization", "secondary": [ "statistics" ], "capabilities": [ "batch_effect_correction", "qc_drift_correction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "R abundance objects with batch, run-order and replicate annotations" ], "other_output_formats": [ "Corrected R abundance objects and diagnostic plots" ] }, "common_uses": [ "Correct within- and between-batch unwanted variation in large metabolomics studies." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-08-16", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "hRUV removes unwanted variation from multi-batch metabolomics data using a hierarchy of within-batch and between-batch corrections. The strategy uses embedded replicate samples with RUV-III and signal-drift smoothers.", "scope": { "designed_for": [ "An R package for hierarchical normalization of multi-batch metabolomics studies using intra-batch and inter-batch replicate information to estimate and remove unwanted variation." ], "not_designed_for": [ "General correction of studies lacking the replicate design needed to estimate technical variation, or raw peak detection." ], "requirements": [ "R/Bioconductor dependencies, processed abundances and sample/run metadata identifying the required intra-batch and inter-batch replicate arrangements." ], "considerations": [ "Replicate placement and connectivity determine what variation can be estimated. Check that correction preserves biological differences and does not confuse them with batch structure." ] }, "publications": [ { "doi": "10.1038/s41467-021-25210-5", "type": "primary", "citation": "A hierarchical approach to removal of unwanted variation for large-scale metabolomics data. Nature Communications (2021)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "iceberg", "name": "ICEBERG", "aliases": [], "summary": "ICEBERG is a two-stage neural network that predicts mass spectra from molecular structures. It can rank candidate structures against a spectrum for metabolite annotation.", "links": { "primary": "https://github.com/coleygroup/ms-pred", "repository": "https://github.com/coleygroup/ms-pred", "documentation": "https://github.com/coleygroup/ms-pred#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "python_package" ], "access": { "model": "open_source", "notes": "Python source, model instructions and links to pretrained weights are provided in the repository. Dataset access and pretrained-model use are distinct from training a new model; consult their respective terms and requirements." }, "publications": [ { "doi": "10.1021/acs.analchem.3c04654", "type": "primary", "citation": "Goldman S, Li J, Coley CW. Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks. Analytical Chemistry 96, 3419-3428 (2024).", "note": "Original ICEBERG 1.0 method." }, { "doi": "10.1101/2025.05.28.656653", "type": "primary", "citation": "Wang R, et al. Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry. bioRxiv (2025). Preprint.", "note": "ICEBERG 2.0 citation listed by the official repository; later repository updates should be identified by model version." } ], "common_uses": [ "Predict MS/MS spectra for candidate structures.", "Rank a defined candidate set by comparison with an experimental spectrum." ], "scope": { "designed_for": [ "Structure-to-spectrum prediction and candidate ranking for small molecules." ], "not_designed_for": [ "Raw chromatographic feature extraction or guaranteed de novo identification from a spectrum." ], "requirements": [ "Use matching model weights, molecular-structure inputs and model-specific acquisition metadata. Follow the prediction example for the chosen ICEBERG version." ], "considerations": [ "Candidate ranking is limited by the supplied candidate set and model applicability.", "The repository distinguishes ICEBERG 1.0, 2.0 and 2.1. Record the model and weights used.", "Training hardware requirements must not be presented as the minimum requirement for pretrained inference.", "Software execution and prediction performance were not tested during this editorial preparation." ] }, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-14" }, "credits": [ { "name": "Runzhong Wang", "role": "developer", "orcid": "0000-0002-9566-738X" } ], "provenance": { "submitted_by": "developer", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/26", "developer_verified": false, "submitter_name": "Runzhong Wang", "public_attribution_consent": true, "submitter_orcid": "0000-0002-9566-738X", "notes": "Draft editorial additions checked against the official ms-pred README and direct ICEBERG publications. Original developer-submission attribution is retained; no additional developer verification is claimed." }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-14", "updated_at": "2026-09-15", "last_verified": "2026-09-14" }, "description": "ICEBERG predicts tandem mass spectra from candidate molecular structures using a learned fragment generator and an intensity predictor. Predicted spectra can be compared with an experimental spectrum to rank supplied candidates. The ms-pred repository also contains separate models; this entry covers ICEBERG, not every method in that repository.", "data": { "input_types": [ "molecular_structures", "spectra" ], "output_types": [ "spectra", "compound_list" ], "input_formats": [ "other" ], "other_input_formats": [ "SMILES candidate structures; experimental spectra for candidate ranking." ], "output_formats": [ "other" ], "other_output_formats": [ "Predicted spectra and candidate rankings; model-specific outputs." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "idbac", "name": "IDBac", "summary": "Web-based MALDI-TOF mass spectrometry platform and community knowledgebase for bacterial identification, culture-collection analysis, and comparison of specialized-metabolite profiles.", "description": "IDBac combines bacterial MALDI protein fingerprints with specialized-metabolite profiles to compare cultured isolates. Its web platform provides reference matching, protein-similarity views and metabolite-association analysis. This entry covers the current platform and its earlier R/desktop methods; older installation instructions are not interchangeable with the current GNPS2 interface.", "links": { "primary": "https://idbac.org/", "web_app": "https://analysis.idbac.org/", "homepage": "https://idbac.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/idbacanalysisplatform/", "repository": "https://github.com/Wang-Bioinformatics-Lab/IDBac_Interactive_Interface" }, "resource_types": [ "web_service", "database_knowledgebase", "workflow_platform" ], "functions": { "primary": "reference_data_search", "secondary": [ "molecular_networking", "visualization_reporting", "data_management_sharing" ], "capabilities": [ "metadata_contextualization", "interactive_dashboard", "network_visualization" ] }, "platforms": [ "maldi_ms" ], "analysis_types": [ "metabolite_profiling" ], "interfaces": [ "web_app" ], "access": { "model": "free_academic", "notes": "The account-request documentation requires a GNPS2 account requested through an academic affiliation and describes a staged closed-beta rollout. Check the current request page before planning an analysis. Public reference or example views do not establish unrestricted analysis access." }, "data": { "input_types": [ "spectra", "sample_metadata" ], "output_types": [ "spectra", "statistical_results", "report" ], "input_formats": [ "mzml", "csv_tsv" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "MALDI protein and specialized-metabolite spectra with plate-map metadata" ], "other_output_formats": [ "Protein dendrograms and heatmaps", "Metabolite association networks and analysis tables" ] }, "publications": [ { "citation": "Clark CM, Costa MS, Sanchez LM, Murphy BT. Coupling MALDI-TOF mass spectrometry protein and specialized metabolite analyses to rapidly discriminate bacterial function. Proceedings of the National Academy of Sciences 115, 4981-4986 (2018).", "type": "primary", "doi": "10.1073/pnas.1801247115", "pmid": "29686101" }, { "citation": "Clark CM, Costa MS, Conley E, Li E, Sanchez LM, Murphy BT. Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data. Journal of Visualized Experiments 147, e59219 (2019).", "type": "methods", "doi": "10.3791/59219", "pmid": "31157770" }, { "citation": "Clark CM, Nguyen L, Pham VC, et al. Automated Microbial Library Generation Using the Bioinformatics Platform IDBac. Molecules 27, 2038 (2022).", "type": "methods", "doi": "10.3390/molecules27072038", "pmid": "35408437" }, { "citation": "Krull NK, Strobel M, Saulog J, et al. IDBac: an open-access web platform to identify bacteria and analyze relationships in culture collections using MALDI-TOF mass spectrometry. Nature Communications 17, 8956 (2026).", "type": "primary", "doi": "10.1038/s41467-026-75579-4", "pmid": "42637730" } ], "common_uses": [ "Compare specialized-metabolite profiles among related bacterial isolates.", "Prioritize culture-collection isolates for follow-up MS analysis." ], "scope": { "designed_for": [ "High-throughput analysis of cultured bacterial isolates using MALDI-TOF or compatible MALDI mass-spectrometry data.", "Connecting taxonomic or protein-fingerprint similarity with differences in specialized-metabolite production.", "Culture-collection dereplication and strain prioritization before labor-intensive scale-up." ], "not_designed_for": [ "Replacing sequence-based taxonomy when definitive taxonomic resolution is required.", "Complete structural elucidation of specialized metabolites from MALDI-MS1 profiles alone." ], "requirements": [ "Reproducible MALDI sample preparation and appropriate replicate acquisition.", "Protein-region spectra for bacterial grouping or identification and, where chemical comparison is desired, lower-mass specialized-metabolite spectra." ], "considerations": [ "Protein spectral similarity is not definitive sequence-based taxonomy.", "Culture conditions, MALDI matrix and acquisition settings affect detected chemical profiles.", "MS1 metabolite-association networks do not establish structures or imply MS/MS fragmentation similarity." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official project documentation and direct tool publication metadata reviewed on 2026-09-12. No account creation, data upload, installation or independent benchmarking performed. Included for MALDI specialized-metabolite profiling and culture-collection interpretation. The separate colony MALDI-MS/MS method paper is not listed as a core platform publication." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "related_tools": [ { "slug": "gnps", "relationship": "integration", "note": "Current account, preparation and analysis routes use the GNPS2 ecosystem." }, { "slug": "msconvert", "relationship": "complementary", "note": "Current IDBac documentation uses MSConvert for conversion of supported raw MALDI files to mzML." }, { "slug": "massive", "relationship": "complementary", "note": "MassIVE can preserve and expose mass-spectrometry datasets associated with IDBac studies." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "idsl-mint", "name": "IDSL_MINT", "summary": "IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations.", "links": { "primary": "https://github.com/idslme/IDSL_MINT", "repository": "https://github.com/idslme/IDSL_MINT", "documentation": "https://github.com/idslme/IDSL_MINT#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "spectral_analysis" ], "capabilities": [ "substructure_prediction", "structure_elucidation", "in_silico_spectra" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "source_available", "notes": "Source is publicly available, but a repository-wide license grant was not established from the reviewed metadata; model and dataset terms must be checked separately." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures", "spectra", "statistical_results" ], "other_input_formats": [ "Module-specific MSP spectra, molecular representations and YAML config" ], "other_output_formats": [ "Fingerprints, candidate structures or predicted spectra by module" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-01-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations. Documented modules include MS/MS-to-fingerprint prediction, structure generation and reverse spectrum prediction, with YAML-controlled training and prediction workflows.", "scope": { "designed_for": [ "IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations." ], "not_designed_for": [ "Definitive structure confirmation or evidence that every documented module was evaluated in the original fingerprint-prediction paper." ], "requirements": [ "Python with PyTorch, RDKit and documented dependencies; prepared MSP/structure data, a module-specific YAML configuration and suitable trained weights or training data." ], "considerations": [ "Select the representation and model for the task. Fingerprint accuracy, structure generation and predicted spectra have different validation needs and chemical-domain limits." ] }, "common_uses": [ "IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations." ], "publications": [ { "doi": "10.1186/s13321-024-00804-5", "type": "primary", "citation": "IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra. Journal of Cheminformatics (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ili", "name": "`ili", "aliases": [ "ili" ], "summary": "Web-based software for interactive visualization of molecular distributions on 2D images and 3D surfaces.", "description": "`ili maps quantitative molecular or omics features onto 2D images or 3D surface models. It accepts a spatial mapping table plus a background image or 3D model and supports reusable visualization snapshots and shareable links.", "links": { "primary": "https://ili.embl.de/", "repository": "https://github.com/MolecularCartography/ili", "documentation": "https://github.com/MolecularCartography/ili#input" }, "resource_types": [ "software_application", "web_service" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "lc_ms", "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "web_app" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 source with a browser interface. Availability of the hosted site and older extension distribution should be checked before a workshop or production workflow." }, "data": { "input_types": [ "imaging_data", "feature_table" ], "output_types": [ "imaging_data" ], "input_formats": [ "csv_tsv" ], "output_formats": [ "json" ], "other_input_formats": [ "PNG/JPG background or STL model", "Textured OBJ model with MTL and image files" ], "other_output_formats": [ "Rendered image and reusable visualization snapshot" ] }, "publications": [ { "citation": "Protsyuk I, Melnik AV, Nothias L-F, et al. 3D molecular cartography using LC-MS facilitated by Optimus and 'ili software. Nature Protocols 13, 134–154 (2018).", "type": "primary", "doi": "10.1038/nprot.2017.122" } ], "common_uses": [ "Visualize molecular features across 2D biological surfaces.", "Map metabolomics features onto 3D models." ], "scope": { "designed_for": [ "Interactive molecular cartography when feature intensities can be associated with spatial coordinates." ], "considerations": [ "Upstream normalization, annotation and statistical validation occur outside this visualization layer.", "Spatial patterns depend on sampling density, coordinate accuracy and display settings." ], "not_designed_for": [ "Raw MS preprocessing, statistical inference or identification from a spatial map alone." ], "requirements": [ "Feature intensities matched to sample coordinates and a corresponding background image or model." ] }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official repository input/output documentation, license and publisher citation checked on 2026-09-11. The Nature Protocols paper introduces the software and method, rather than merely using it. Hosted rendering and data processing were not independently tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_editorial_review": "2026-09-11", "last_verified": "2026-09-11" }, "related_tools": [ { "slug": "mzmine", "relationship": "complementary", "note": "An upstream LC-MS feature-processing option; a coordinate mapping table still needs preparation." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "imputomics", "name": "imputomics", "summary": "An R package and Shiny application for simulating and imputing missing values across omics datasets, including metabolomics, with multiple imputation algorithms and evaluation utilities.", "links": { "primary": "https://github.com/BioGenies/imputomics", "repository": "https://github.com/BioGenies/imputomics", "documentation": "https://biogenies.info/imputomics/" }, "resource_types": [ "programming_package", "web_service" ], "functions": { "primary": "statistics", "secondary": [ "visualization_reporting" ], "capabilities": [ "multivariate_statistics", "interactive_dashboard" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling", "multiomics" ], "interfaces": [ "r_package", "web_app" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 according to package DESCRIPTION. The hosted server and underlying imputation packages have separate operational and dependency requirements; local analysis avoids uploading study data." }, "technical": { "operating_systems": [ "browser", "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table" ], "output_types": [ "feature_table", "statistical_results" ], "other_input_formats": [ "R numeric matrices or tables containing missing values" ], "other_output_formats": [ "Imputed matrices, simulation/evaluation summaries and Shiny plots" ] }, "common_uses": [ "Compare and apply missing-value imputation methods to metabolomics tables." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-11-07", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "imputomics wraps multiple missing-value imputation methods and provides simulations for comparing their behavior. It supports R and Shiny interfaces for omics matrices, including metabolomics, with missingness-mechanism scenarios and error summaries.", "scope": { "designed_for": [ "An R package and Shiny application for simulating and imputing missing values across omics datasets, including metabolomics, with multiple imputation algorithms and evaluation utilities." ], "not_designed_for": [ "Recovering the true unmeasured concentration with certainty or proving that simulated missingness matches the study mechanism." ], "requirements": [ "R and the selected method's dependencies; a numeric data matrix with correctly represented missing values and suitable evaluation data for simulation-based comparison." ], "considerations": [ "Choose imputation assumptions using acquisition and preprocessing context. Prevent train/test leakage and evaluate how imputation changes downstream statistics rather than comparing reconstruction error alone." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btae098", "type": "primary", "citation": "imputomics: web server and R package for missing values imputation in metabolomics data. Bioinformatics (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "injectiondesign", "name": "InjectionDesign", "summary": "A web service for designing LC-MS and GC-MS multi-omics injection sequences with QC placement, inter-batch balancing, intra-batch randomization, and downloadable worksheets.", "links": { "primary": "http://www.injection.design/", "web_app": "http://www.injection.design/", "repository": "https://github.com/CSi-Studio/InjectionDesign", "documentation": "https://github.com/CSi-Studio/InjectionDesign#readme" }, "resource_types": [ "web_service" ], "functions": { "primary": "quality_control", "secondary": [ "data_management_sharing" ], "capabilities": [ "metadata_contextualization" ] }, "platforms": [ "lc_ms", "gc_ms" ], "analysis_types": [ "multiomics" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "The repository carries MulanPSL-2.0 terms and describes a free hosted service. Hosted availability and user-data handling were not operationally tested.", "license": "MulanPSL-2.0" }, "technical": { "operating_systems": [ "browser", "cross_platform" ] }, "data": { "input_types": [ "sample_metadata" ], "output_types": [ "sample_metadata" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Excel worksheet" ], "other_output_formats": [ "Excel worksheet" ] }, "common_uses": [ "Randomize and balance study samples across analytical batches.", "Place pooled, blank, solvent, reference, and custom QC samples within an injection sequence." ], "maintenance": { "status": "unclear", "repository_last_activity": "2023-11-27", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Prospective injection-order design and QC placement before LC-MS or GC-MS acquisition." ], "not_designed_for": [ "Correcting instrument drift or batch effects after acquisition, or replacing experimental design review." ], "requirements": [ "A sample Excel sheet using the provided template, grouping variables and desired plate/QC settings. Local deployment uses Java, MongoDB and Redis; the hosted route uses a browser." ], "considerations": [ "Balance and randomization dimensions must reflect the scientific design; inspect the exported sequence and QC spacing before acquisition." ] }, "description": "InjectionDesign prepares acquisition sequences and plate layouts from sample metadata. It combines inter-batch balancing, within-batch randomization and predefined QC placement before LC/GC-MS measurements.", "publications": [ { "doi": "10.1186/s12859-023-05598-1", "type": "primary", "citation": "Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation. BMC Bioinformatics (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "iontoolpack", "name": "IonToolPack", "summary": "IonToolPack groups graphical mass-spectrometry utilities for raw-data visualization, PeakQC quality assessment, tandem-library matching, targeted MS1 extraction and feature-list comparison.", "links": { "primary": "https://github.com/pnnl/IonToolPack", "repository": "https://github.com/pnnl/IonToolPack", "documentation": "https://github.com/pnnl/IonToolPack#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "quality_control", "secondary": [ "visualization_reporting" ], "capabilities": [ "interactive_dashboard", "library_matching", "multivariate_statistics" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "lipidomics" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "BSD-2-Clause", "notes": "BSD-2-Clause source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "windows" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "feature_table", "compound_list", "report" ], "other_input_formats": [ "Supported vendor/mzML files, target lists and MSP/CSV libraries" ], "other_output_formats": [ "QC metrics, extracted peak tables, match results and PDF/CSV exports" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-24", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "IonToolPack groups graphical mass-spectrometry utilities for raw-data visualization, PeakQC quality assessment, tandem-library matching, targeted MS1 extraction and feature-list comparison. The suite supports LC-MS, ion-mobility and direct-infusion workflows through documented readers.", "scope": { "designed_for": [ "IonToolPack groups graphical mass-spectrometry utilities for raw-data visualization, PeakQC quality assessment, tandem-library matching, targeted MS1 extraction and feature-list comparison." ], "not_designed_for": [ "Automatic confirmation of library matches or calibrated concentration determination from peak abundance alone." ], "requirements": [ "A compatible desktop release and supported raw files; targeted extraction needs target settings and tandem matching needs MSP or CSV reference libraries." ], "considerations": [ "Module capabilities differ. Vendor-reader compatibility, QC thresholds and matching tolerances must be checked for the acquisition; the listed primary paper describes PeakQC within the suite." ] }, "common_uses": [ "IonToolPack groups graphical mass-spectrometry utilities for raw-data visualization, PeakQC quality assessment, tandem-library matching, targeted MS1 extraction and feature-list comparison." ], "publications": [ { "doi": "10.1021/jasms.4c00146", "type": "primary", "citation": "PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data. Journal of the American Society for Mass Spectrometry (2024).", "note": "PeakQC module within the IonToolPack suite; not a publication introducing every later suite component." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ipo", "name": "IPO", "summary": "Optimize peak picking, retention-time correction and grouping parameters for XCMS.", "description": "IPO evaluates processing settings using isotope information and reproducibility across pooled-sample injections, helping replace arbitrary parameter selection.", "links": { "primary": "https://bioconductor.org/packages/IPO/", "documentation": "https://bioconductor.org/packages/IPO/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "peak_detection", "retention_time_alignment" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Open-source software; use the official installation, license and citation guidance for the selected release." }, "data": { "input_types": [ "spectra" ], "output_types": [ "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Files supported by the configured XCMS reader" ], "other_output_formats": [ "R lists of optimized parameters and scores" ] }, "publications": [ { "citation": "Libiseller G, Dvorzak M, Kleb U, et al. IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16, 118 (2015).", "type": "primary", "doi": "10.1186/s12859-015-0562-8" } ], "common_uses": [ "Tune an LC-HRMS method before processing a study.", "Compare candidate settings using representative QC injections." ], "scope": { "designed_for": [ "Tune an LC-HRMS method before processing a study." ], "not_designed_for": [ "Replacing chromatogram inspection or guaranteeing a universally optimal parameter set." ], "requirements": [ "R, compatible XCMS dependencies and representative optimization files." ], "considerations": [ "Optimization reflects the samples and parameter ranges supplied; validate the chosen settings on the wider study." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "xcms", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "isfrag", "name": "ISFrag", "summary": "ISFrag recognizes candidate in-source fragments in LC-MS data by combining feature relationships and available MS/MS evidence.", "links": { "primary": "https://github.com/HuanLab/ISFrag", "repository": "https://github.com/HuanLab/ISFrag", "documentation": "https://github.com/HuanLab/ISFrag#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "secondary": [ "annotation_identification", "preprocessing" ], "capabilities": [ "adduct_isotope_grouping" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "feature_table", "compound_list" ], "other_input_formats": [ "mzXML LC-MS files or CSV peak tables with m/z, RT and intensities" ], "other_output_formats": [ "Annotated feature tables and in-source-fragment relationship plots" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2022-09-06", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "ISFrag recognizes candidate in-source fragments in LC-MS data by combining feature relationships and available MS/MS evidence. Its R workflow accepts XCMS-derived features or a custom peak table and supports multiple levels of fragment annotation.", "scope": { "designed_for": [ "ISFrag recognizes candidate in-source fragments in LC-MS data by combining feature relationships and available MS/MS evidence." ], "not_designed_for": [ "Confirming a metabolite structure from a coeluting feature relationship alone or automatically deleting all proposed fragments." ], "requirements": [ "R and documented XCMS dependencies, mzXML files or correctly ordered CSV peak tables; retention times must use seconds and MS/MS evidence must match the selected workflow." ], "considerations": [ "Adduct/isotope processing through CAMERA belongs to the XCMS route. Distinguish evidence levels and retain ambiguous coeluting alternatives." ] }, "common_uses": [ "ISFrag recognizes candidate in-source fragments in LC-MS data by combining feature relationships and available MS/MS evidence." ], "publications": [ { "doi": "10.1021/acs.analchem.1c01644", "type": "primary", "citation": "ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography–Mass Spectrometry Data. Analytical Chemistry (2021)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "isopairfinder", "name": "IsoPairFinder", "summary": "An R package for identifying biologically relevant 12C/13C feature pairs in stable-isotope-tracing metabolomics, including differential analysis and redundant-feature merging.", "links": { "primary": "https://github.com/DoddLab/IsoPairFinder", "repository": "https://github.com/DoddLab/IsoPairFinder", "documentation": "https://doddlab.github.io/IsoPairFinder_Tutorials/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "isotope_tracing_flux", "secondary": [ "statistics", "preprocessing" ], "capabilities": [ "adduct_isotope_grouping", "univariate_statistics" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "stable_isotope_tracing" ], "interfaces": [ "r_package" ], "access": { "model": "source_available", "license": "CC-BY-NC-ND-4.0", "notes": "README specifies CC-BY-NC-ND-4.0 while DESCRIPTION retains a placeholder license field; no unrestricted open-source claim is made. The GNPS2 workflow is a separate hosted route." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "isotope_tracing_data", "feature_table", "sample_metadata" ], "output_types": [ "isotope_tracing_data", "statistical_results" ], "other_input_formats": [ "Labelled/unlabelled CSV peak tables, sample metadata and mzML files" ], "other_output_formats": [ "Candidate isotope-pair/intermediate tables and supporting analysis results" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-09", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "IsoPairFinder prioritizes candidate microbial pathway intermediates by combining genetic perturbation with isotope-tracing LC-MS data. It compares groups, merges redundant ion features and pairs labelled/unlabelled signals for experimental follow-up.", "scope": { "designed_for": [ "An R package for identifying biologically relevant 12C/13C feature pairs in stable-isotope-tracing metabolomics, including differential analysis and redundant-feature merging." ], "not_designed_for": [ "Flux estimation, general isotopologue natural-abundance correction or proof that a prioritized pair is a specific pathway intermediate." ], "requirements": [ "R dependencies, labelled and unlabelled peak tables, matching sample/group/tracer metadata, and appropriately organized mzML raw/MS2 files as described in the tutorial." ], "considerations": [ "Tracer design, genetic perturbation, thresholds and feature pairing constrain interpretation. Retain alternative explanations and validate prioritized candidates experimentally." ] }, "common_uses": [ "An R package for identifying biologically relevant 12C/13C feature pairs in stable-isotope-tracing metabolomics, including differential analysis and redundant-feature merging." ], "publications": [ { "doi": "10.1021/jasms.6c00253", "type": "primary", "citation": "IsoPairFinder: A Computational Workflow for Prioritizing LC–MS Feature Pairs Representing Candidate Microbial Pathway Intermediates from Stable Isotope Tracing Metabolomics. Journal of the American\nSociety for Mass Spectrometry (2026)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "isoscan", "name": "isoSCAN", "summary": "Integrate stable-isotope-labeling peaks from GC chemical-ionization mass spectrometry data.", "description": "isoSCAN is an R package for automatic peak integration in low- and high-resolution GC-CI-MS labeling experiments. Its catalogue scope follows the official package description; LC-MS support is not inferred from the discovery list.", "links": { "primary": "https://github.com/jcapelladesto/isoSCAN", "repository": "https://github.com/jcapelladesto/isoSCAN", "documentation": "https://github.com/jcapelladesto/isoSCAN" }, "platforms": [ "gc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "isotope_tracing_flux", "capabilities": [ "peak_detection" ] }, "access": { "model": "open_source", "license": "GPL-3.0", "notes": "R package depending on mzR and other packages declared in DESCRIPTION." }, "data": { "input_types": [ "spectra", "isotope_tracing_data" ], "output_types": [ "isotope_tracing_data" ], "other_input_formats": [ "GC-CI-MS labeling data supplied through the documented R workflow" ], "other_output_formats": [ "Integrated isotope-labeling peak measurements returned by the R workflow" ] }, "common_uses": [ "Process GC-CI-MS measurements from stable-isotope experiments." ], "scope": { "designed_for": [ "Peak integration for GC chemical-ionization labeling data." ], "not_designed_for": [ "A general LC-MS workflow or independent confirmation of metabolic flux." ], "requirements": [ "GC-CI-MS measurements and the repository R dependencies; consult the package vignette for import preparation." ], "considerations": [ "Match ionization mode and isotope experiment to the documented workflow." ] }, "publications": [ { "doi": "10.1021/acs.analchem.0c02998", "type": "primary", "citation": "Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labeling in Metabolomics. Analytical Chemistry (2020).", "note": "Published online December 2020; journal issue 2021. Both dates can appear in citations." } ], "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Discovered through the Enveda review intake; official repository and primary publication metadata checked on 2026-10-02. Editorial review only; software and performance not execution-tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "jpa", "name": "JPA", "summary": "JPA combines MS1 chromatographic peak picking with MS/MS-precursor-based feature extraction to broaden LC-MS feature detection.", "links": { "primary": "https://github.com/HuanLab/JPA", "repository": "https://github.com/HuanLab/JPA", "documentation": "https://github.com/HuanLab/JPA#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [], "capabilities": [ "peak_detection", "retention_time_alignment" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "raw_instrument_data", "feature_table", "spectra" ], "output_types": [ "feature_table" ], "other_input_formats": [ "mzXML LC-MS/MS data and optional formatted CSV peak tables" ], "other_output_formats": [ "Jointly extracted and aligned LC-MS feature tables" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2023-04-06", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "JPA combines MS1 chromatographic peak picking with MS/MS-precursor-based feature extraction to broaden LC-MS feature detection. The R workflow supports alignment and downstream handling of the jointly extracted feature set.", "scope": { "designed_for": [ "JPA combines MS1 chromatographic peak picking with MS/MS-precursor-based feature extraction to broaden LC-MS feature detection." ], "not_designed_for": [ "Metabolite structure identification or recovery of compounds lacking sufficient acquisition evidence." ], "requirements": [ "R/XCMS dependencies and mzXML acquisition files with appropriate MS/MS precursor information; follow the documented peak-table layout when supplying existing features." ], "considerations": [ "Additional precursor-derived features still require quality review. Peak-picking, alignment and acquisition thresholds affect the balance between added coverage and unwanted features." ] }, "common_uses": [ "JPA combines MS1 chromatographic peak picking with MS/MS-precursor-based feature extraction to broaden LC-MS feature detection." ], "publications": [ { "doi": "10.3390/metabo12030212", "type": "primary", "citation": "JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics. Metabolites (2022)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "khipu", "name": "khipu", "summary": "Group LC-MS isotope and adduct features into empirical compounds and infer neutral masses.", "description": "khipu provides Python and command-line pre-annotation of feature tables. Its graph/tree representation links related ions; the associated Khipu-web publication extends access for stable-isotope tracing. These groupings do not by themselves establish molecular identity.", "links": { "primary": "https://github.com/shuzhao-li-lab/khipu", "repository": "https://github.com/shuzhao-li-lab/khipu", "documentation": "https://github.com/shuzhao-li-lab/khipu" }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "command_line" ], "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "adduct_isotope_grouping" ] }, "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "Python package; the repository also links a web interface. Check that interface separately for hosted availability." }, "data": { "input_types": [ "feature_table" ], "output_types": [ "compound_list" ], "other_input_formats": [ "Tab-delimited feature ID, m/z, retention time and sample intensity columns" ], "other_output_formats": [ "Empirical-compound annotations in JSON and TSV" ] }, "common_uses": [ "Reduce redundant isotope/adduct features before downstream interpretation." ], "scope": { "designed_for": [ "LC-MS ion grouping, including configured isotope-tracing patterns." ], "not_designed_for": [ "Confirmed structural identification or a metabolic-flux solver." ], "requirements": [ "A preprocessed feature table and appropriate ion-mode, mass and retention-time tolerances." ], "considerations": [ "Grouping depends on expected ion patterns and preprocessing quality." ] }, "publications": [ { "doi": "10.1021/acs.analchem.2c05810", "type": "primary", "citation": "Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data. Analytical Chemistry (2023)." }, { "doi": "10.1021/jasms.4c00175", "type": "methods", "citation": "Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web. Journal of the American Society for Mass Spectrometry (2024)." } ], "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Discovered through the Enveda review intake; official repository and primary publication metadata checked on 2026-10-02. Editorial review only; software and performance not execution-tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" }, "related_tools": [ { "slug": "asari", "relationship": "integration", "note": "asari uses khipu for pre-annotation." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lc-ms2struct", "name": "LC-MS2Struct", "summary": "Jointly rank molecular candidates using LC retention order and precomputed MS/MS scores.", "description": "LC-MS2Struct uses a structured support vector machine to combine relative elution order with spectral candidate scores across a set of features. It returns candidate rankings after preprocessing and candidate generation; it does not generate an end-to-end workflow from raw files.", "links": { "primary": "https://github.com/aalto-ics-kepaco/msms_rt_ssvm", "repository": "https://github.com/aalto-ics-kepaco/msms_rt_ssvm", "documentation": "https://github.com/aalto-ics-kepaco/lcms2struct_exp" }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "structure_elucidation" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "MIT license verified in the official repository. Software was not installed or execution-tested; dependencies and external services have their own requirements." }, "data": { "input_types": [ "compound_list", "feature_table" ], "output_types": [ "compound_list" ], "other_input_formats": [ "Candidate structures, MS/MS scores and retention times via the Python interface" ], "other_output_formats": [ "Per-feature molecular candidate scores and rankings" ] }, "common_uses": [ "Re-rank ambiguous candidates using the retention order shared across an LC-MS/MS run." ], "scope": { "designed_for": [ "Jointly rank molecular candidates using LC retention order and precomputed MS/MS scores." ], "not_designed_for": [ "Replacing experimental confirmation of compound identity.", "Raw-data feature detection or chromatographic alignment." ], "requirements": [ "Linux and the documented Conda environment; the README does not support other operating systems.", "The separate msmsrt_scorer dependency, suitable model/training data, per-feature candidate structures and MS/MS scores, and measured retention times.", "Prepare inputs using the linked manuscript experiment scripts; this is a research Python implementation." ], "considerations": [ "The published evaluation uses reversed-phase LC data; evaluate transfer to your chromatographic conditions.", "Candidates absent from the input sets cannot be recovered by re-ranking.", "Joint scores depend on candidate and model quality; a high rank does not establish stereochemistry." ] }, "publications": [ { "doi": "10.1038/s42256-022-00577-2", "type": "primary", "citation": "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data. Nature Machine Intelligence (2022)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake reviewed against the primary publication, official repository documentation and license on 2026-10-05. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-05" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-05", "updated_at": "2026-10-05", "last_verified": "2026-10-05", "last_editorial_review": "2026-10-05" }, "aliases": [ "LC-MS²Struct" ], "related_tools": [ { "slug": "cfm-id", "relationship": "integration", "note": "The primary paper demonstrates integration of candidate scores from CFM-ID. This is not a claim of a one-click connector." }, { "slug": "metfrag", "relationship": "integration", "note": "The primary paper demonstrates integration of candidate scores from MetFrag. This is not a claim of a one-click connector." }, { "slug": "sirius", "relationship": "integration", "note": "The primary paper demonstrates integration of candidate scores from SIRIUS. This is not a claim of a one-click connector." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lcmsworld", "name": "lcmsWorld", "summary": "lcmsWorld is an interactive three-dimensional viewer for LC-MS data.", "links": { "primary": "https://github.com/PGB-LIV/lcmsWorld", "repository": "https://github.com/PGB-LIV/lcmsWorld", "documentation": "https://github.com/PGB-LIV/lcmsWorld#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "visualization_reporting", "secondary": [], "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "AGPL-3.0", "notes": "AGPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "windows", "linux", "macos" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "report" ], "other_input_formats": [ "mzML or supported Thermo RAW data and optional annotation tables" ], "other_output_formats": [ "Interactive 3D LC-MS views and reusable lcms cache files" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2021-11-23", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "lcmsWorld is an interactive three-dimensional viewer for LC-MS data. It converts supported acquisitions into a streamed visualization file, allowing users to rotate and inspect the mass/retention-time landscape and overlay supplied annotations.", "scope": { "designed_for": [ "lcmsWorld is an interactive three-dimensional viewer for LC-MS data." ], "not_designed_for": [ "Automatic alignment, ion-mobility analysis or metabolite identification; these are not established by the displayed data landscape." ], "requirements": [ "OpenGL 3.0 or newer, adequate disk space and RAM; Windows releases are supplied, Linux requires compilation and macOS builds are documented as needing attention." ], "considerations": [ "Initial loading creates an lcms cache file. Thermo RAW reading additionally depends on the 64-bit .NET reader and platform compatibility." ] }, "common_uses": [ "lcmsWorld is an interactive three-dimensional viewer for LC-MS data." ], "publications": [ { "doi": "10.1021/acs.jproteome.0c00618", "type": "primary", "citation": "lcmsWorld: High-Performance 3D Visualization Software for Mass Spectrometry. Journal of Proteome Research (2021)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lion-web", "name": "LION/web", "summary": "Web-based lipid ontology enrichment tool that connects lipid lists or rankings to chemical, biophysical, cellular and functional lipid properties.", "links": { "primary": "https://www.lipidontology.com/", "documentation": "https://www.lipidontology.com/faq.html" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "pathway_interpretation", "capabilities": [ "pathway_enrichment" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Free hosted service without an account requirement. The FAQ excludes confidential or proprietary uploads; review the current terms before submitting data." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official service documentation and dedicated tool citation reviewed on 2026-09-11. Included for downstream MS lipidomics interpretation. No user dataset was uploaded or enrichment analysis run." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "lipidomics" ], "data": { "input_types": [ "compound_list", "feature_table", "statistical_results" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "csv_tsv" ], "other_input_formats": [ "Lipid lists or rankings, depending on analysis mode" ], "other_output_formats": [ "Ontology enrichment results and interactive reports" ] }, "publications": [ { "citation": "Molenaar MR, Jeucken A, Wassenaar TA, et al. LION/web: a web-based ontology enrichment tool for lipidomic data analysis. GigaScience 8, giz061 (2019).", "type": "primary", "doi": "10.1093/gigascience/giz061" } ], "description": "LION/web interprets lipidomics through enrichment of lipid ontology terms. These terms cover chemical and biophysical properties and biological associations, rather than only metabolic pathways. The service operates downstream of lipid identification and quantification.", "common_uses": [ "Identify lipid properties enriched among changed species.", "Interpret ranked quantitative lipidomics results." ], "scope": { "designed_for": [ "Ontology-based interpretation of annotated lipidomics data." ], "not_designed_for": [ "Raw MS processing or proving pathway activity from enrichment alone." ], "requirements": [ "Recognized lipid names and correctly formatted input for the chosen mode.", "The quantitative CSV mode requires condition and sample identifiers." ], "considerations": [ "Check which lipid names match the ontology before interpreting results.", "The selected background, rankings and identification confidence affect enrichment." ] }, "related_tools": [ { "slug": "lipidr", "relationship": "complementary", "note": "Upstream quantitative lipidomics analysis can supply interpreted lipid lists; direct export compatibility is not assumed." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipid-maps", "name": "LIPID MAPS", "summary": "Find lipid structures, classifications, nomenclature guidance and lipidomics analysis resources.", "description": "LIPID MAPS brings together curated lipid databases, mass-search tools and educational material, supporting consistent reporting and reference lookup.", "links": { "primary": "https://www.lipidmaps.org/", "documentation": "https://www.lipidmaps.org/" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Free open-access resource; consult the terms for each database, tool or download." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Lipid names, identifiers, structures or mass queries" ], "other_output_formats": [ "Lipid records and selected database exports" ] }, "publications": [ { "doi": "10.1093/nar/gkad896", "citation": "Conroy MJ, Andrews RM, Andrews S, et al. LIPID MAPS: update to databases and tools for the lipidomics community. Nucleic Acids Res 52, D1677-D1682 (2024).", "type": "primary" } ], "resource_citations": [ { "citation": "LIPID MAPS consortium. LIPID MAPS: databases, classification, nomenclature and analysis tools. Official resource documentation.", "type": "documentation", "url": "https://www.lipidmaps.org/" } ], "common_uses": [ "Check lipid abbreviations and structural classification.", "Find mass-compatible lipid candidates and supporting references." ], "scope": { "designed_for": [ "Check lipid abbreviations and structural classification." ], "not_designed_for": [ "Resolving acyl-chain positions or double-bond locations from a mass-only match." ], "requirements": [ "A lipid name, structure, identifier or appropriately specified mass query." ], "considerations": [ "Report the structural level supported by the experiment; database structures can be more specific than the measured evidence." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "lipidblast", "relationship": "complementary" }, { "slug": "refmet", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipid-mini-on", "name": "Lipid Mini-On", "summary": "Group lipid names into ontology terms and test enrichment using a locally run Shiny interface.", "links": { "primary": "https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn", "documentation": "https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn", "repository": "https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn" }, "resource_types": [ "software_application" ], "functions": { "primary": "pathway_interpretation", "capabilities": [ "pathway_enrichment", "network_visualization" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "open_source", "license": "BSD-2-Clause", "notes": "Local browser interface served by R/Shiny. The current README requires R 4.x, RStudio, Rodin and listed R dependencies; do not assume the historical hosted endpoint remains available." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation and dedicated software publications reviewed on 2026-09-11. No installation, uploaded dataset or independent benchmark was run." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "lipidomics" ], "data": { "input_types": [ "compound_list" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "csv_tsv" ], "other_output_formats": [ "Ontology enrichment tables and interactive plots" ] }, "publications": [ { "citation": "Clair G, Reehl S, Stratton KG, et al. Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data. Bioinformatics 35, 4507–4508 (2019).", "type": "primary", "doi": "10.1093/bioinformatics/btz250", "pmid": "30977807" } ], "description": "Lipid Mini-On uses the Rodin R package to group lipid names by classes and chain properties, perform enrichment tests and visualize lipid-to-term relationships. Current repository instructions describe local execution.", "common_uses": [ "Compare a lipid query set against its measured background.", "Explore lipid classes and chain properties associated with a selected set." ], "scope": { "designed_for": [ "Ontology enrichment of lipid name lists." ], "not_designed_for": [ "Raw MS data processing or direct metabolic-flux inference." ], "requirements": [ "Query and universe lipid lists in the documented CSV layout, plus local R dependencies." ], "considerations": [ "Background selection and name parsing affect enrichment; inspect assignments before interpreting terms." ] }, "related_tools": [ { "slug": "lion-web", "relationship": "alternative", "note": "Another lipid ontology enrichment tool; term definitions and statistical workflows differ." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipida-ider", "name": "LipidA-IDER", "summary": "LipidA-IDER annotates lipid A structures from high-resolution tandem mass spectra using lipid A-specific fragmentation interpretation.", "links": { "primary": "https://github.com/Systems-Biology-Of-Lipid-Metabolism-Lab/LipidA-IDER", "repository": "https://github.com/Systems-Biology-Of-Lipid-Metabolism-Lab/LipidA-IDER", "documentation": "https://github.com/Systems-Biology-Of-Lipid-Metabolism-Lab/LipidA-IDER#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "secondary": [ "spectral_analysis" ], "capabilities": [ "structure_elucidation", "ms2_processing" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "free_academic", "license": "NTUITIVE non-commercial dual license", "notes": "NTUITIVE license permits specified non-commercial uses and requires a separate commercial agreement. Its distribution clauses conflict; consult the full license before redistribution." }, "technical": { "operating_systems": [ "windows" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "other_input_formats": [ "MS2 spectra plus parameter and batch CSV templates" ], "other_output_formats": [ "Lipid A annotation results and timestamped analysis logs" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2022-12-12", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "LipidA-IDER annotates lipid A structures from high-resolution tandem mass spectra using lipid A-specific fragmentation interpretation. It provides graphical and command-line workflows with configurable analysis parameters and batch processing.", "scope": { "designed_for": [ "LipidA-IDER annotates lipid A structures from high-resolution tandem mass spectra using lipid A-specific fragmentation interpretation." ], "not_designed_for": [ "General lipidomics identification or unambiguous assignment of all lipid A positional and stereochemical isomers." ], "requirements": [ "The documented Python environment or Windows application, MS2 files prepared with the specified MSConvert peak-picking workflow and configured analysis/batch CSV templates." ], "considerations": [ "Only Windows operation is documented as tested. Verify fragmentation settings, candidate chemistry and annotation evidence for the organism and experiment." ] }, "common_uses": [ "LipidA-IDER annotates lipid A structures from high-resolution tandem mass spectra using lipid A-specific fragmentation interpretation." ], "publications": [ { "doi": "10.1021/acs.analchem.1c03566", "type": "primary", "citation": "LipidA-IDER to Explore the Global Lipid A Repertoire of Drug-Resistant Gram-Negative Bacteria. Analytical Chemistry (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidblast", "name": "LipidBlast", "summary": "Use an in-silico tandem mass-spectral library to support lipid annotation.", "description": "LipidBlast provides computer-generated spectra for lipid classes, designed for searching with compatible spectral-library software.", "links": { "primary": "https://fiehnlab.ucdavis.edu/projects/LipidBlast", "documentation": "https://fiehnlab.ucdavis.edu/projects/LipidBlast" }, "resource_types": [ "spectral_library" ], "functions": { "primary": "reference_data_search", "capabilities": [ "library_matching" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "desktop_gui" ], "access": { "model": "free_web_service", "notes": "Downloadable reference library for use in a compatible external search engine. Check the distribution terms of both before reuse." }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "input_formats": [ "mgf" ], "output_formats": [ "other" ], "other_output_formats": [ "Search-engine-dependent candidate matches" ] }, "publications": [ { "citation": "Kind T, Liu KH, Lee DY, et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods 10, 755-758 (2013).", "type": "primary", "doi": "10.1038/nmeth.2551" } ], "common_uses": [ "Compare lipid MS/MS spectra against predicted references.", "Extend candidate coverage beyond available experimental standards." ], "scope": { "designed_for": [ "Compare lipid MS/MS spectra against predicted references." ], "not_designed_for": [ "Treating predicted reference spectra as measurements from authentic standards." ], "requirements": [ "MS/MS spectra and a search tool that accepts the selected library distribution." ], "considerations": [ "Class, adduct and fragmentation coverage are finite; match quality does not resolve all lipid structural details." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "ms-dial", "relationship": "complementary" }, { "slug": "lipid-maps", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidcreator", "name": "LipidCreator", "summary": "Design targeted lipidomics assays and predicted fragment libraries for PRM and MRM experiments.", "links": { "primary": "https://lifs-tools.org/tools/lipidcreator.html", "documentation": "https://github.com/lifs-tools/lipidcreator", "repository": "https://github.com/lifs-tools/lipidcreator" }, "resource_types": [ "software_application" ], "functions": { "primary": "data_acquisition", "secondary": [ "spectral_analysis" ], "capabilities": [ "in_silico_spectra" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "open_source", "license": "MIT", "notes": "Install through the Skyline Tool Store or use the standalone application. Windows instructions are documented; Linux uses Mono with reported GUI limitations. Consult release-specific requirements." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation and dedicated software publications reviewed on 2026-09-11. No installation, uploaded dataset or independent benchmark was run." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "targeted", "lipidomics" ], "data": { "input_types": [ "compound_list" ], "output_types": [ "spectral_library", "report" ], "other_input_formats": [ "User-defined lipid classes, chains, adducts and fragments" ], "other_output_formats": [ "Precursor-product transition lists", "BLIB spectral libraries" ] }, "publications": [ { "citation": "Peng B, Kopczynski D, Pratt BS, et al. LipidCreator workbench to probe the lipidomic landscape. Nature Communications 11, 2057 (2020).", "type": "primary", "doi": "10.1038/s41467-020-15960-z", "pmid": "32345972" } ], "related_tools": [ { "slug": "skyline", "relationship": "integration", "note": "Designed for targeted assay export/integration with Skyline." } ], "description": "LipidCreator calculates lipid precursor and fragment masses from user-defined lipid building blocks. It supports isotope-labelled targets and predicted spectral libraries, with direct Skyline integration or standalone use.", "common_uses": [ "Prepare lipid transition lists for targeted acquisition.", "Generate predicted lipid fragment libraries for Skyline workflows." ], "scope": { "designed_for": [ "Assay design for supported lipid classes." ], "not_designed_for": [ "Automatic proof that predicted transitions are selective in a biological matrix." ], "requirements": [ "Target lipid definitions and compatible runtime; Skyline for integrated workflows." ], "considerations": [ "Validate transition selectivity and collision energies experimentally for the intended instrument." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidetective", "name": "LipiDetective", "summary": "A deep-learning framework for identifying molecular lipid species from tandem mass spectra.", "links": { "primary": "https://github.com/LipiTUM/lipidetective", "repository": "https://github.com/LipiTUM/lipidetective", "documentation": "https://lipitum.github.io/lipidetective/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation", "compound_class_prediction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "BSD-3-Clause source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "other_input_formats": [ "mzML spectra for prediction; HDF5 data and YAML for training" ], "other_output_formats": [ "Predicted lipid shorthand annotations and model-dependent scores" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-08-09", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "LipiDetective predicts molecular lipid species in standardized shorthand notation from tandem spectra using a transformer model. It offers model training with prepared data and inference for supported spectra, providing a complementary source of annotation evidence.", "scope": { "designed_for": [ "A deep-learning framework for identifying molecular lipid species from tandem mass spectra." ], "not_designed_for": [ "Confirming double-bond positions or stereochemistry not resolved by the model, or accepting every generated lipid name without checking precursor compatibility." ], "requirements": [ "Python environment, YAML configuration and trained checkpoint; the documented training route uses HDF5 data and prediction uses mzML files." ], "considerations": [ "Training-set lipid/adduct and instrument coverage affect performance. Inspect predictions against precursor mass and diagnostic fragments; the current paper discusses training bias and missing hard precursor-mass constraints." ] }, "common_uses": [ "A deep-learning framework for identifying molecular lipid species from tandem mass spectra." ], "publications": [ { "doi": "10.1093/bib/bbag378", "type": "primary", "citation": "LipiDetective: a deep learning model for the identification of molecular lipid species in tandem mass spectra. Briefings in Bioinformatics (2026)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidex-2", "name": "LipiDex 2", "summary": "A standalone LC-MS lipidomics suite for in-silico library generation, MS/MS and MSn spectral searching, lipid feature annotation, peak-table integration, and automated identification quality control.", "links": { "primary": "https://github.com/coongroup/LipiDex-2", "repository": "https://github.com/coongroup/LipiDex-2", "documentation": "https://github.com/coongroup/LipiDex-2/wiki", "download": "https://github.com/coongroup/LipiDex-2/releases/latest" }, "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "secondary": [ "spectral_analysis", "quality_control", "quantification" ], "capabilities": [ "in_silico_spectra", "library_matching", "identification_confidence", "ms2_processing" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "desktop_gui" ], "access": { "model": "source_available", "notes": "Public source and executable releases are available, but no explicit repository-wide software license was identified. Compound Discoverer is a separate commercial dependency for Peak Finder." }, "technical": { "operating_systems": [ "windows" ], "programming_languages": [ "C#" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra", "feature_table", "spectral_library" ], "output_types": [ "compound_list", "feature_table", "report" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "Thermo raw, spectral libraries and Compound Discoverer cdResult by module" ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c00359", "type": "primary", "citation": "LipiDex 2 Integrates MSn Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics. Analytical Chemistry (2024)." } ], "common_uses": [ "Generate or search lipid spectral libraries and annotate LC-MS lipid features.", "Apply retention-time, spectral-match, linearity, QC-RSD, and other evidence to filter lipid identifications." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-08-17", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "LC-MS lipid identification and quality-controlled lipidomics workflows." ], "requirements": [ "Windows with .NET 7 and appropriate lipid spectra/libraries. Peak Finder specifically requires Compound Discoverer 3.3 cdResult input; other modules can run separately." ], "considerations": [ "Input requirements differ by module. Quality-control criteria need the relevant dilution series, replicate QCs or other evidence and do not confer experimental confirmation by themselves." ], "not_designed_for": [ "A universal raw-data peak detector or unqualified assignment of lipid structural detail beyond the acquired fragmentation evidence." ] }, "acquisition": { "ms_levels": [ "ms2", "msn" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "unspecified" }, "description": "LipiDex 2 provides lipid spectral-library generation, spectral matching, peak annotation and quality-control modules. It extends lipidomics analysis to MSn fragmentation trees and includes Degreaser checks based on retention behavior, dilution response, spectral evidence and QC variability." }, { "$schema": "../../schemas/tool.schema.json", "access": { "model": "open_source", "license": "MIT", "notes": "MIT declaration verified in official repository documentation and metadata. No installation or execution test performed." }, "common_uses": [ "Filter LC-MS lipid features and obtain putative database annotations." ], "data": { "input_types": [ "feature_table" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "Prepared LC-MS feature CSV files and JSON workflow parameters" ], "other_output_formats": [ "Filtered and annotated CSV tables; lipid-category plots" ] }, "description": "LipidFinder processes previously extracted lipidomics feature tables, removes artifacts, optionally combines polarities and searches lipid databases. This canonical entry includes version 2.0.", "functions": { "primary": "preprocessing", "capabilities": [ "blank_filtering", "structure_database_search" ] }, "interfaces": [ "command_line" ], "links": { "primary": "https://github.com/ODonnell-Lipidomics/LipidFinder", "repository": "https://github.com/ODonnell-Lipidomics/LipidFinder", "documentation": "https://github.com/ODonnell-Lipidomics/LipidFinder#readme" }, "maintenance": { "last_checked": "2026-10-21", "status": "unclear" }, "name": "LipidFinder", "platforms": [ "lc_ms" ], "provenance": { "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark.", "submitted_by": "curator" }, "publications": [ { "doi": "10.1093/bioinformatics/btaa856", "type": "methods", "citation": "LipidFinder 2.0: advanced informatics pipeline for lipidomics discovery applications. Bioinformatics (2021)." }, { "doi": "10.1172/jci.insight.91634", "type": "primary", "citation": "LipidFinder: A computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets. JCI Insight (2017)." } ], "resource_types": [ "workflow_pipeline" ], "scope": { "designed_for": [ "LipidFinder processes previously extracted lipidomics feature tables, removes artifacts, optionally combines polarities and searches lipid databases. This canonical entry includes version 2.0." ], "not_designed_for": [ "Shotgun lipidomics, low-resolution data or definitive MS/MS structure identification." ], "requirements": [ "High-resolution LC-MS data processed into feature tables upstream, with suitable chromatographic separation.", "Python dependencies and documented column/parameter preparation." ], "considerations": [ "Database mass matches are putative; inspect filtering and confirm identities with additional evidence." ] }, "slug": "lipidfinder", "status": { "created_at": "2026-10-21", "entry": "published", "last_editorial_review": "2026-10-21", "last_verified": "2026-10-21", "review": "editorially_reviewed", "updated_at": "2026-10-21" }, "summary": "Filter LC-MS lipid features and obtain putative database annotations.", "analysis_types": [ "lipidomics" ], "aliases": [ "LipidFinder 2.0" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidlynxx", "name": "LipidLynxX", "functions": { "primary": "data_management_sharing", "capabilities": [ "identifier_harmonization" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "lipidomics" ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official repository and documentation reviewed on 2026-09-11. Included for downstream MS lipidomics. No installation, parser execution or user-data upload performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-11", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "data": { "input_types": [ "compound_list" ], "output_types": [ "compound_list" ], "other_input_formats": [ "Lipid shorthand names" ], "other_output_formats": [ "Standardized lipid identifiers" ] }, "summary": "Convert, cross-match and link lipid names across supported nomenclatures before downstream MS lipidomics interpretation.", "description": "LipidLynxX provides Converter, Equalizer and Linker modules for nomenclature conversion, comparison at selected structural levels and resource linking. Its naming workflow can prepare lipid lists for tools such as BioPAN.", "links": { "primary": "https://www.lipidmaps.org/lipidlynxx/", "documentation": "https://github.com/SysMedOs/LipidLynxX", "repository": "https://github.com/SysMedOs/LipidLynxX" }, "resource_types": [ "web_service", "programming_package", "api_service" ], "interfaces": [ "web_app", "python_package", "api", "command_line" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "Hosted on LIPID MAPS, with source available for local deployment. Local use requires a compatible Python environment and dependencies; consult repository setup instructions." }, "common_uses": [ "Harmonize names before combining lipidomics tables.", "Compare lipid lists at a chosen annotation level and obtain resource links." ], "scope": { "designed_for": [ "Supported lipid shorthand conversion and identifier cross-matching." ], "not_designed_for": [ "Raw MS processing or experimental confirmation of lipid structures." ], "requirements": [ "Lipid names in a supported notation and a chosen conversion or comparison level." ], "considerations": [ "Check unmatched names and conversions before downstream analysis.", "Preserve the structural resolution supported by the original measurements." ] }, "publications": [ { "citation": "Ni Z, Fedorova M. LipidLynxX: a data transfer hub to support integration of large scale lipidomics datasets. bioRxiv (2020).", "doi": "10.1101/2020.04.09.033894", "type": "methods", "note": "Preprint; the hosted service cites this dedicated software description." } ], "related_tools": [ { "slug": "biopan", "relationship": "integration", "note": "BioPAN documents optional LipidLynxX conversion of lipid nomenclature." }, { "slug": "goslin", "relationship": "alternative", "note": "Another approach to lipid naming consistency; grammar support and outputs differ." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidoz", "name": "LipidOz", "summary": "Determine lipid carbon–carbon double-bond positions using diagnostic ozone-induced dissociation data.", "links": { "primary": "https://lipidoz.readthedocs.io/en/latest/", "documentation": "https://lipidoz.readthedocs.io/en/latest/", "repository": "https://github.com/PNNL-m-q/lipidoz" }, "resource_types": [ "programming_package", "software_application" ], "functions": { "primary": "annotation_identification", "secondary": [ "spectral_analysis" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "interfaces": [ "python_package", "desktop_gui" ], "access": { "model": "open_source", "license": "BSD-2-Clause", "notes": "Python source and GUI executables are available. The repository labels its linked GUI release as beta; match the executable and documentation versions before analysis." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation and dedicated software publications reviewed on 2026-09-11. No installation, uploaded dataset or independent benchmark was run. Reconciled with the LipidOz candidate in PR #13 (628a0f9); preserved one canonical slug, corrected the master draft author and verified BSD-2-Clause licensing. PR #13 was merged upstream during this work; preserved its other records and this single enriched LipidOz entry." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "lipidomics" ], "data": { "input_types": [ "spectra", "compound_list" ], "output_types": [ "compound_list", "report" ], "input_formats": [ "csv_tsv" ], "other_input_formats": [ "MZA OzID data" ], "other_output_formats": [ "LipidOz binary results", "Excel scoring reports" ] }, "publications": [ { "citation": "Ross DH, Lee JY, Bilbao A, et al. LipidOz enables automated elucidation of lipid carbon–carbon double bond positions from ozone-induced dissociation mass spectrometry data. Communications Chemistry 6, 74 (2023).", "doi": "10.1038/s42004-023-00867-9", "type": "primary" } ], "description": "LipidOz scores isotope-distribution evidence for candidate OzID fragments and provides a graphical review interface. Its documented workflows include LC and infusion analyses; ordinary collision-induced MS/MS is not a substitute for OzID measurements.", "common_uses": [ "Score candidate lipid double-bond positions from OzID fragments.", "Review fragment evidence and export target-level scoring reports." ], "scope": { "designed_for": [ "Structural interpretation of compatible OzID lipid measurements." ], "not_designed_for": [ "General lipid discovery without OzID data." ], "requirements": [ "OzID data, a compatible target list and workflow-specific extraction settings. The LC guide uses MZA data and CSV lipid/adduct/retention-time targets." ], "considerations": [ "Inspect diagnostic-fragment quality and ambiguous assignments; a software score alone is not structural confirmation." ] }, "credits": [], "related_tools": [ { "slug": "mza", "relationship": "uses", "note": "The documented LC isotope-scoring workflow consumes MZA OzID data." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidqmap", "name": "LipidQMap", "summary": "LipidQMap visualizes and quantifies lipid mass-spectrometry images using user-defined internal standards.", "links": { "primary": "https://github.com/swinnenteam/LipidQMap", "repository": "https://github.com/swinnenteam/LipidQMap", "documentation": "https://github.com/swinnenteam/LipidQMap#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "quantification", "secondary": [ "visualization_reporting", "quality_control" ], "capabilities": [ "isotopologue_correction", "interactive_dashboard" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "lipidomics", "spatial_metabolomics" ], "interfaces": [ "desktop_gui" ], "access": { "model": "free_academic", "license": "KU Leuven research-only license", "notes": "License permits research use and copying, excludes commercial and third-party contract-research services, and requests permission for modification/dissemination rights. It is not an unrestricted open-source grant." }, "data": { "input_types": [ "imaging_data", "sample_metadata" ], "output_types": [ "imaging_data", "feature_table", "report" ], "other_input_formats": [ "imzML, compatible h5ad/h5mu MSI data and lipid/standard tables" ], "other_output_formats": [ "Raw/corrected/quantified ion images and image-panel exports" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-08-26", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "LipidQMap visualizes and quantifies lipid mass-spectrometry images using user-defined internal standards. It supports type-II isotopic-overlap corrections, selected protonated/sodiated overlap correction and comparison of raw, corrected and quantified images.", "scope": { "designed_for": [ "LipidQMap visualizes and quantifies lipid mass-spectrometry images using user-defined internal standards." ], "not_designed_for": [ "Absolute quantitation without appropriate standards or general structural identification from an ion-image mass match." ], "requirements": [ "Supported desktop application or source environment, imzML or schema-compatible AnnData/MuData MSI data, an editable lipid list and appropriate internal standards for quantitation." ], "considerations": [ "Mass tolerances, isotope corrections, optional interpolation and calibration settings affect images. AnnData/MuData imports require the documented mass, ion-mode and spatial metadata." ] }, "common_uses": [ "LipidQMap visualizes and quantifies lipid mass-spectrometry images using user-defined internal standards." ], "publications": [ { "doi": "10.1101/2025.10.15.682422", "type": "primary", "citation": "LipidQMap - An Open-Source Tool for Quantitative Mass Spectrometry Imaging of Lipids. bioRxiv preprint (2025). Preprint; not peer reviewed." } ], "technical": { "operating_systems": [ "windows", "macos" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidr", "name": "lipidr", "summary": "Analyze lipid abundance tables and test patterns across lipid classes and structural properties.", "description": "lipidr supports inspection, normalization, differential analysis and lipid-set enrichment, including class, chain-length and unsaturation patterns.", "links": { "primary": "https://bioconductor.org/packages/lipidr/", "documentation": "https://bioconductor.org/packages/lipidr/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "capabilities": [ "univariate_statistics", "multivariate_statistics", "pathway_enrichment" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Bioconductor package; consult its license and the separate terms of imported public datasets." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Numeric matrix or Skyline export" ], "other_output_formats": [ "R lipid-analysis objects, statistics and plots" ] }, "publications": [ { "doi": "10.1021/acs.jproteome.0c00082", "citation": "Mohamed A, Molendijk J, Hill MM. lipidr: A Software Tool for Data Mining and Analysis of Lipidomics Datasets. J Proteome Res 19, 2890-2897 (2020).", "type": "primary" } ], "resource_citations": [ { "citation": "lipidr developers. lipidr: Data Mining and Analysis of Lipidomics Datasets. Bioconductor package record.", "type": "software", "doi": "10.18129/B9.bioc.lipidr" } ], "common_uses": [ "Analyze targeted or untargeted lipidomics tables.", "Investigate coordinated changes among related lipid species." ], "scope": { "designed_for": [ "Analyze targeted or untargeted lipidomics tables." ], "not_designed_for": [ "Inferring positional lipid structures that were not resolved by the measurement." ], "requirements": [ "A lipid abundance matrix or compatible Skyline export, with sample metadata and parseable lipid names." ], "considerations": [ "Lipid naming and structural resolution determine the sets available for enrichment; review ambiguous or unparsed entries." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "skyline", "relationship": "complementary" }, { "slug": "lipid-maps", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidsig", "name": "LipidSig", "summary": "Explore lipid abundance tables through statistical analysis, lipid characteristics, enrichment and network visualizations.", "links": { "primary": "https://lipidsig.bioinfomics.org/", "documentation": "https://lipidsig.bioinfomics.org/Tutorial/" }, "resource_types": [ "web_service" ], "functions": { "primary": "statistics", "secondary": [ "pathway_interpretation", "visualization_reporting" ], "capabilities": [ "univariate_statistics", "multivariate_statistics", "pathway_enrichment", "network_visualization" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Free hosted service open to all users. Data are uploaded for analysis; consult the service before submitting sensitive datasets. No open-source license is inferred for the web service from the companion R package." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation and dedicated software publications reviewed on 2026-09-11. No installation, uploaded dataset or independent benchmark was run." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "lipidomics" ], "data": { "input_types": [ "feature_table", "compound_list" ], "output_types": [ "statistical_results", "report", "pathway_data" ], "input_formats": [ "csv_tsv" ], "other_output_formats": [ "Downloadable result tables", "PDF plots" ] }, "publications": [ { "citation": "Lin WJ, Shen PC, Liu HC, et al. LipidSig: a web-based tool for lipidomic data analysis. Nucleic Acids Research 49, W336–W345 (2021).", "doi": "10.1093/nar/gkab419", "type": "primary" }, { "citation": "Liu CH, Shen PC, Lin WJ, et al. LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis. Nucleic Acids Research 52, W390–W397 (2024).", "type": "methods", "doi": "10.1093/nar/gkae335", "pmid": "38709887" } ], "description": "LipidSig 2.0 provides a browser workflow for data checking, lipid-name mapping and downstream analysis of processed lipidomics tables. The canonical entry covers the original and updated web service; the separate LipidSigR package is linked by the service but is not treated as identical software.", "common_uses": [ "Compare lipid abundances and characteristics across study groups.", "Inspect enrichment and lipid association networks." ], "scope": { "designed_for": [ "Downstream analysis of processed MS lipidomics tables." ], "not_designed_for": [ "Raw spectral peak picking or experimental structure identification." ], "requirements": [ "CSV abundance tables and analysis-specific sample information; use Data Check first." ], "considerations": [ "Inspect unrecognized lipid names and preprocessing choices.", "Enrichment and network modules use differential-expression output; statistical associations require biological validation." ] }, "related_tools": [ { "slug": "lion-web", "relationship": "alternative", "note": "Another lipid enrichment option, with different ontology and input conventions." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidsigr", "name": "LipidSigR", "summary": "Analyze lipid abundance changes and lipid characteristics in R.", "description": "LipidSigR supports lipid profiling, differential analysis, enrichment, correlation and selected network or machine-learning workflows using processed lipidomics data.", "links": { "primary": "https://github.com/BioinfOMICS/LipidSigR", "repository": "https://github.com/BioinfOMICS/LipidSigR", "documentation": "https://lipidsig.bioinfomics.org/lipidsigr/" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "capabilities": [ "univariate_statistics", "multivariate_statistics", "pathway_enrichment" ] }, "access": { "model": "open_source", "notes": "License checked against official repository/package metadata. Installation and execution were not tested.", "license": "MIT" }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Processed lipid abundance and sample information in package input objects" ], "other_output_formats": [ "R analysis objects, result tables and visualizations" ] }, "common_uses": [ "Analyze lipid abundance changes and lipid characteristics in R." ], "scope": { "designed_for": [ "LipidSigR supports lipid profiling, differential analysis, enrichment, correlation and selected network or machine-learning workflows using processed lipidomics data." ], "not_designed_for": [ "Raw LC-MS preprocessing or independent validation of lipid identities." ], "requirements": [ "R 4.3 or later, package dependencies, lipid identifiers and a suitable study design." ], "considerations": [ "The documented network and machine-learning workflows are restricted to two-group data.", "Results depend on lipid naming, preprocessing and study design; predictive models require appropriate validation." ] }, "publications": [ { "doi": "10.1093/bioadv/vbaf047", "type": "primary", "citation": "LipidSigR: a R-based solution for integrated lipidomics data analysis and visualization. Bioinformatics Advances (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" }, "analysis_types": [ "lipidomics" ], "related_tools": [ { "slug": "lipidsig", "relationship": "complementary", "note": "Companion R package based on the LipidSig web tool; interfaces and workflow support differ." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lipidspace", "name": "LipidSpace", "summary": "LipidSpace compares lipidomes using graph-based distances between lipid structures and quantitative information.", "links": { "primary": "https://github.com/lifs-tools/lipidspace", "repository": "https://github.com/lifs-tools/lipidspace", "documentation": "https://lifs-tools.org/tools/lipidspace.html" }, "resource_types": [ "software_application" ], "functions": { "primary": "statistics", "secondary": [ "visualization_reporting", "quality_control" ], "capabilities": [ "multivariate_statistics", "network_visualization" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "windows", "macos", "linux" ] }, "data": { "input_types": [ "feature_table", "compound_list" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "CSV/XLSX lipid tables or mzTab-M lipidomics records" ], "other_output_formats": [ "Lipidome distances, statistical/feature results and PDF visualizations" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-09-04", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "LipidSpace compares lipidomes using graph-based distances between lipid structures and quantitative information. Its desktop interface supports experiment comparison, study-variable exploration, feature selection and quality-control views.", "scope": { "designed_for": [ "LipidSpace compares lipidomes using graph-based distances between lipid structures and quantitative information." ], "not_designed_for": [ "Raw spectral lipid identification or proof of biological similarity independent of annotation quality and structural resolution." ], "requirements": [ "A supported desktop release and identified lipid abundance tables with compatible lipid nomenclature and sample/study-variable annotations." ], "considerations": [ "Lipid naming, structural detail, sn-position assumptions and distance/clustering settings influence comparisons. This resource is distinct from the LORA paper incorrectly associated with its repository in the intake." ] }, "common_uses": [ "LipidSpace compares lipidomes using graph-based distances between lipid structures and quantitative information." ], "publications": [ { "doi": "10.1021/acs.analchem.3c02449", "type": "primary", "citation": "LipidSpace: Simple Exploration, Reanalysis, and Quality Control of Large-Scale Lipidomics Studies. Analytical Chemistry (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lotus", "name": "LOTUS", "summary": "Connect natural-product structures, organism occurrences and literature references.", "description": "LOTUS harmonizes natural-product occurrence knowledge and makes it available through a dedicated interface and Wikidata, supporting community curation.", "links": { "primary": "https://lotus.naturalproducts.net/", "documentation": "https://lotus.naturalproducts.net/" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Open knowledge initiative; preserve provenance and consult the terms of the selected export." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Structures, taxa or occurrence queries" ], "other_output_formats": [ "Structure-organism-reference records" ] }, "publications": [ { "citation": "Rutz A, Sorokina M, Galgonek J, et al. The LOTUS initiative for open knowledge management in natural products research. Elife 11, e70780 (2022).", "type": "primary", "doi": "10.7554/eLife.70780" } ], "common_uses": [ "Explore reported natural products for a taxon.", "Build candidate lists with traceable literature context." ], "scope": { "designed_for": [ "Explore reported natural products for a taxon." ], "not_designed_for": [ "Treating database absence as evidence that an organism cannot produce a molecule." ], "requirements": [ "A chemical structure, taxon or other supported query." ], "considerations": [ "Coverage reflects published and curated observations; taxonomic assignments and occurrence records need source review." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "coconut", "relationship": "complementary" }, { "slug": "natural-products-atlas", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "lpptiger2", "name": "LPPtiger 2", "aliases": [ "LPPtiger2" ], "summary": "LPPtiger 2 supports epilipidomics by predicting modified lipid candidates and their fragmentation patterns, preparing acquisition inclusion lists and annotating modified lipids in LC-MS experiments.", "links": { "primary": "https://github.com/LMAI-TUD/lpptiger2", "repository": "https://github.com/LMAI-TUD/lpptiger2", "download": "https://github.com/LMAI-TUD/lpptiger2/releases", "documentation": "https://github.com/LMAI-TUD/lpptiger2#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "secondary": [ "spectral_analysis" ], "capabilities": [ "in_silico_spectra", "structure_elucidation" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "desktop_gui", "python_package" ], "access": { "model": "free_academic", "license": "AGPL-3.0", "notes": "README offers AGPLv3 for academic/non-commercial use and requests contact for commercial use. This restricted statement is recorded rather than an unrestricted open-source grant." }, "technical": { "operating_systems": [ "windows", "macos", "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra", "molecular_structures" ], "output_types": [ "spectra", "compound_list" ], "other_input_formats": [ "Lipid/modification configurations and manual-specified LC-MS data" ], "other_output_formats": [ "Predicted modified lipids, fragments, inclusion lists and annotations" ] }, "publications": [ { "doi": "10.1038/s41467-022-33225-9", "type": "primary", "citation": "Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma. Nature Communications (2022)." } ], "common_uses": [ "Predict and annotate oxidized or otherwise modified lipid species.", "Generate in-silico fragmentation and inclusion lists for epilipidomics workflows." ], "maintenance": { "status": "unclear", "repository_last_activity": "2022-10-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "LPPtiger 2 supports epilipidomics by predicting modified lipid candidates and their fragmentation patterns, preparing acquisition inclusion lists and annotating modified lipids in LC-MS experiments." ], "not_designed_for": [ "General identification of every lipid class or definitive localization of all oxidation and modification sites." ], "requirements": [ "A supported desktop executable or documented Python environment, configured lipid/modification rules and LC-MS inputs organized according to the supplied manual and test data." ], "considerations": [ "Candidate coverage depends on configured modification chemistry and fragmentation rules. Inspect diagnostic evidence and competing assignments before reporting structures." ] }, "description": "LPPtiger 2 supports epilipidomics by predicting modified lipid candidates and their fragmentation patterns, preparing acquisition inclusion lists and annotating modified lipids in LC-MS experiments. Its interface includes a graphical application and Python source workflow." }, { "$schema": "../../schemas/tool.schema.json", "slug": "m2aia", "name": "M²aia", "aliases": [ "M2aia" ], "summary": "Open-source environment for interactive processing, visualization and registration of two- and three-dimensional mass spectrometry imaging data.", "description": "M²aia extends the Medical Imaging Interaction Toolkit with MSI signal processing and image interaction. Researchers can inspect ion images and reconstruct or register spatial datasets. The related pyM²aia project exposes programmatic functionality separately.", "links": { "primary": "https://m2aia.de/", "repository": "https://github.com/m2aia/m2aia", "documentation": "https://m2aia.de/" }, "resource_types": [ "software_application" ], "functions": { "primary": "visualization_reporting", "secondary": [ "preprocessing" ], "capabilities": [ "spectral_data_io", "peak_detection", "container_support" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "Source and Windows/Linux packages are available. Dependencies and companion software may carry separate licenses." }, "data": { "input_types": [ "imaging_data" ], "input_formats": [ "imzml" ], "output_types": [ "imaging_data" ], "output_formats": [ "imzml" ] }, "common_uses": [ "Interactively inspect and process MSI spectra and ion images.", "Register multimodal images and reconstruct three-dimensional MSI datasets." ], "scope": { "designed_for": [ "Interactive spatial-data exploration and MSI image processing." ], "not_designed_for": [ "Definitive metabolite identification from image colocalization alone." ], "requirements": [ "A compatible Windows or Linux installation, or the documented container environment.", "The project specifies elastix for image-registration utilities." ], "considerations": [ "Inspect registration quality and processing parameters before interpreting spatial overlap.", "Memory and storage requirements depend on image size and processing choices.", "Python workflows use the companion pyM²aia interface; desktop and Python capabilities are not assumed identical." ] }, "publications": [ { "citation": "Cordes J, Enzlein T, Marsching C, Hinze M, Engelhardt S, Hopf C, Wolf I. M²aia—Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data. GigaScience 10(7), giab049 (2021).", "doi": "10.1093/gigascience/giab049", "pmid": "34282451", "type": "primary" } ], "related_tools": [ { "slug": "cardinal", "relationship": "alternative", "note": "An R-based alternative for MSI analysis; feature sets and interfaces differ." }, { "slug": "pyimzml", "relationship": "complementary", "note": "Programmatic imzML I/O can support preparation of imaging data; direct interoperability was not tested." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official project site, repository and publication metadata reviewed on 2026-09-11. The desktop tool paper is included; companion-interface and incidental application papers are not attributed to the desktop release. No installation, registration or benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-11", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "maffin", "name": "MAFFIN", "summary": "Normalize metabolomics feature intensities using selected features and corrected signal responses.", "description": "MAFFIN combines feature-quality selection, signal-intensity correction and maximal-density fold-change normalization. Its R implementation returns normalized intensities, normalization factors and variation summaries.", "links": { "primary": "https://github.com/HuanLab/MAFFIN", "repository": "https://github.com/HuanLab/MAFFIN", "documentation": "https://github.com/HuanLab/MAFFIN" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "capabilities": [ "feature_selection" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "License checked against official package metadata or repository. Local setup requires the documented dependencies; no installation or hosted-service availability test was performed." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "CSV/data frame with features, samples and QC-group annotations" ], "other_output_formats": [ "Normalized table, normalization factors and PRMAD summaries as an R list or CSV" ] }, "common_uses": [ "Normalize processed feature tables using the documented QC design." ], "scope": { "designed_for": [ "Normalize metabolomics feature intensities using selected features and corrected signal responses." ], "not_designed_for": [ "Confirming compound identity or replacing study design and scientific review." ], "requirements": [ "An appropriately annotated feature table; serial QC loading amounts for the signal-correction component.", "Feature-by-sample CSV/data frame with group annotations for blanks, QC and serial QC loading levels" ], "considerations": [ "Do not assume that missing serial-QC information can be reconstructed from ordinary study samples.", "Normalization can change biological contrasts; evaluate its assumptions for the study." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btac355", "type": "primary", "citation": "MAFFIN: metabolomics sample normalization using maximal density fold change with high-quality metabolic features and corrected signal intensities. Bioinformatics (2022)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate verified against official documentation, source/package metadata and primary-publication DOI/title/year on 2026-10-02. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mamsi", "name": "MAMSI", "summary": "Integrate multiple LC-MS assays using multiblock statistical models.", "description": "MAMSI combines assay data blocks with multiblock partial least squares, feature-importance assessment and feature-link exploration using m/z and retention time.", "links": { "primary": "https://github.com/kopeckylukas/py-mamsi", "repository": "https://github.com/kopeckylukas/py-mamsi", "documentation": "https://kopeckylukas.github.io/py-mamsi/" }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "capabilities": [ "multivariate_statistics", "feature_selection", "network_visualization" ] }, "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "License verified in the official repository. Code licensing does not establish the terms for every separately downloaded model or dataset." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Assay-specific abundance matrices, matched sample metadata and response values" ], "other_output_formats": [ "Python model/results objects, feature scores and network visualizations" ] }, "common_uses": [ "Integrate multiple LC-MS assays using multiblock statistical models." ], "scope": { "designed_for": [ "MAMSI combines assay data blocks with multiblock partial least squares, feature-importance assessment and feature-link exploration using m/z and retention time." ], "not_designed_for": [ "Raw LC-MS processing or universal metabolite identification." ], "requirements": [ "Python and documented dependencies; sample-matched assay blocks and a numeric modeled response.", "Feature m/z and retention-time metadata for structural-link searches." ], "considerations": [ "Built-in ROI-based annotation is supported only for the documented National Phenome Centre assays.", "Use appropriate cross-validation and avoid information leakage; structural links and predictive associations are not confirmed identities or causal relationships." ] }, "publications": [ { "doi": "10.1021/acs.analchem.5c01327", "type": "primary", "citation": "MAMSI: Integration of Multiassay Liquid Chromatography–Mass Spectrometry Metabolomics Data Using Multiview Machine Learning. Analytical Chemistry (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mass2adduct", "name": "mass2adduct", "summary": "mass2adduct analyzes pairwise mass differences to investigate possible adducts in MSI data.", "links": { "primary": "https://github.com/kbseah/mass2adduct", "repository": "https://github.com/kbseah/mass2adduct", "documentation": "https://github.com/kbseah/mass2adduct#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "preprocessing" ], "capabilities": [ "adduct_isotope_grouping" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "imaging_data", "feature_table" ], "output_types": [ "compound_list", "feature_table" ], "other_input_formats": [ "Mass lists, processed MSI CSV tables or peak-binned Cardinal objects" ], "other_output_formats": [ "R mass-difference objects, histograms and candidate-adduct summaries" ] }, "publications": [ { "doi": "10.1021/acs.analchem.0c04720", "type": "primary", "citation": "Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets. Analytical Chemistry (2021)." } ], "maintenance": { "status": "unclear", "repository_last_activity": "2021-06-09", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "mass2adduct analyzes pairwise mass differences to investigate possible adducts in MSI data. Its R functions import processed matrices or mass lists, summarize difference distributions and compare them with candidate adduct information.", "scope": { "designed_for": [ "mass2adduct analyzes pairwise mass differences to investigate possible adducts in MSI data." ], "not_designed_for": [ "Definitive adduct identification from a mass difference alone or automatic raw-imaging preprocessing through every import route." ], "requirements": [ "R and a mass list, processed MSI CSV matrix or peak-binned Cardinal object; select mass-error/binning settings appropriate to the instrument." ], "considerations": [ "Pairwise combinations can be numerous and include coincidental differences. Spatial/intensity evidence and experimental chemistry help assess candidate adduct assignments." ] }, "common_uses": [ "mass2adduct analyzes pairwise mass differences to investigate possible adducts in MSI data." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mass2smiles", "name": "Mass2SMILES", "summary": "Mass2SMILES uses deep learning to predict candidate molecular structures and functional groups from tandem mass spectra.", "links": { "primary": "https://github.com/volvox292/mass2smiles", "repository": "https://github.com/volvox292/mass2smiles", "documentation": "https://github.com/volvox292/mass2smiles#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation", "substructure_prediction" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "source_available", "notes": "Public source and separately hosted artifacts are available; no repository-wide license grant was established. README states a valid NIST license is required for the supplementary model/container resources." }, "technical": { "operating_systems": [ "linux", "containers" ], "programming_languages": [ "Python" ], "containers": [ "Docker" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures" ], "other_input_formats": [ "GNPS-style MGF tandem spectra" ], "other_output_formats": [ "Candidate SMILES structures and functional-group predictions" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-01-31", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "Mass2SMILES uses deep learning to predict candidate molecular structures and functional groups from tandem mass spectra. The documented inference route reads GNPS-style MGF and uses containerized models with a separate molecular-descriptor component.", "scope": { "designed_for": [ "Mass2SMILES uses deep learning to predict candidate molecular structures and functional groups from tandem mass spectra." ], "not_designed_for": [ "Confirmed structural identification from a prediction alone or unrestricted redistribution of NIST-derived model resources." ], "requirements": [ "Docker and downloaded model/container artifacts, compatible GPU/software settings for the chosen model release and a valid NIST license where required by the supplied artifacts." ], "considerations": [ "The project describes separate GPU model and CPU descriptor containers because of CUDA compatibility limitations. Prediction reliability depends on spectral preparation and training-domain coverage." ] }, "common_uses": [ "Mass2SMILES uses deep learning to predict candidate molecular structures and functional groups from tandem mass spectra." ], "publications": [ { "doi": "10.1101/2023.07.06.547963", "type": "primary", "citation": "Mass2SMILES: deep learning based fast prediction of structures and functional groups directly from high-resolution MS/MS spectra. bioRxiv preprint (2023). Preprint; not peer reviewed." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "massbank", "name": "MassBank", "summary": "Find and download open reference mass spectra with compound and acquisition metadata.", "description": "MassBank is a community reference-spectra resource. Its European and Japanese services share records maintained in a versioned data repository. Records connect peaks with chemical identifiers and measurement conditions, supporting traceable library-based annotation.", "links": { "primary": "https://massbank.eu/", "homepage": "https://massbank.eu/", "documentation": "https://github.com/MassBank/MassBank-data" }, "resource_types": [ "spectral_library", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "library_matching", "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public reference records and downloadable releases are available. Preserve attribution and inspect the license recorded with the data you reuse." }, "data": { "input_types": [ "compound_list", "spectra" ], "output_types": [ "spectral_library", "spectra" ], "other_input_formats": [ "Web search fields and spectral queries" ], "other_output_formats": [ "MassBank record text", "Versioned MassBank data archive" ] }, "common_uses": [ "Inspect reference spectra and their acquisition conditions.", "Select a reproducible library release for an annotation workflow." ], "scope": { "designed_for": [ "Reference-spectrum discovery and reuse." ], "not_designed_for": [ "Archiving complete experimental studies with sample-level abundance tables." ], "requirements": [ "Choose records compatible with ionization, polarity and fragmentation conditions of your experiment." ], "considerations": [ "Library matching depends on coverage and acquisition conditions. Save record accessions and the library release; similarity alone may not distinguish isomers." ] }, "publications": [ { "doi": "10.1093/nar/gkaf1193", "citation": "Neumann S, Meier R, Wenk M, et al. MassBank: an open and FAIR mass spectral data resource. Nucleic Acids Res 54, D601-D606 (2026).", "type": "primary" } ], "related_tools": [ { "slug": "mona", "relationship": "complementary" }, { "slug": "ms-finder", "relationship": "complementary" } ], "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review on 7 September 2026 against official documentation and the sources listed here. Scope and practical guidance are editorial synthesis; software execution and service availability were not validated." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "aliases": [ "MassBank Europe", "MassBank Japan" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "masscube", "name": "MassCube", "summary": "An integrated Python package for LC-MS data processing with nontargeted peak detection and segmentation, feature quality evaluation, feature-group annotation, and MS/MS library or analog searching.", "links": { "primary": "https://huaxuyu.github.io/masscubedocs/docs/quickstart/", "homepage": "https://huaxuyu.github.io/masscubedocs/", "repository": "https://github.com/huaxuyu/masscube", "documentation": "https://huaxuyu.github.io/masscubedocs/docs/quickstart/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [ "quality_control", "annotation_identification", "spectral_analysis" ], "capabilities": [ "chromatogram_building", "peak_detection", "gap_filling", "adduct_isotope_grouping", "library_matching", "ms2_processing" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 source distribution. Native Bruker reading is restricted to documented TDF2/DDA-PASEF and calibration variants; it is not general support for every Bruker acquisition." }, "technical": { "operating_systems": [ "linux", "macos", "windows", "cross_platform" ], "programming_languages": [ "Python" ], "package_managers": [ "PyPI" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "feature_table", "spectra", "compound_list" ], "input_formats": [ "mzml", "mzxml", "vendor_raw" ], "output_formats": [ "csv_tsv", "msp" ] }, "publications": [ { "doi": "10.1038/s41467-025-60640-5", "type": "primary", "citation": "MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers. Nature Communications (2025)." } ], "common_uses": [ "Process LC-MS data from raw spectra to aligned quantitative features.", "Evaluate feature quality and group isotopes, adducts, and in-source fragments.", "Search MS/MS spectra against identity or analog references." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-08-25", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Integrated Python-based LC-MS preprocessing and feature-level annotation." ], "considerations": [ "The native Bruker reader reports inverse mobility without proprietary per-frame pressure compensation. Keep original acquisition files even when using workflow caches." ], "not_designed_for": [ "Universal vendor-file conversion or confirming molecular structures from feature grouping or analog matches." ], "requirements": [ "Python, mzML or mzXML data, and workflow parameters. Supply sample/QC metadata for normalization and statistics and a compatible MS/MS library for spectral annotation." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "unspecified" ], "ion_mobility": "supported" }, "description": "MassCube processes LC-MS data into aligned feature tables and supports feature-quality evaluation, ion grouping and spectral-library annotation. Its command-line workflow combines raw files, sample information and parameters; available normalization and annotation steps depend on the supplied metadata and reference libraries." }, { "$schema": "../../schemas/tool.schema.json", "slug": "massformer", "name": "MassFormer", "summary": "A research implementation of a graph-transformer model for predicting small-molecule tandem mass spectra from molecular structures.", "links": { "primary": "https://github.com/Roestlab/massformer", "repository": "https://github.com/Roestlab/massformer", "documentation": "https://github.com/Roestlab/massformer#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "secondary": [ "machine_learning" ], "capabilities": [ "in_silico_spectra" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "BSD-2-Clause", "notes": "BSD-2-Clause code. Public demo data and checkpoints are provided separately; reproducing NIST-based experiments additionally requires separately licensed NIST data." }, "technical": { "operating_systems": [ "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "spectra" ], "other_output_formats": [ "CSV of predicted spectra and associated metadata" ], "other_input_formats": [ "CSV containing molecular identifiers and SMILES" ] }, "publications": [ { "doi": "10.1038/s42256-024-00816-8", "type": "primary", "citation": "Tandem mass spectrum prediction for small molecules using graph transformers. Nature Machine Intelligence (2024)." } ], "common_uses": [ "Predict MS/MS spectra for small molecules from structure.", "Evaluate or benchmark learned spectrum-prediction models." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-08-26", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Research and benchmarking of learned small-molecule MS/MS spectrum prediction." ], "requirements": [ "A Unix-like Python/PyTorch environment, model checkpoint and molecular SMILES. GPU acceleration is recommended for larger jobs; documented CPU inference is also available." ], "considerations": [ "Predictions depend on molecular and collision-condition coverage; unsupported or unparseable SMILES can be omitted by the inference script." ], "not_designed_for": [ "De novo recovery of a molecular structure from an experimental spectrum or automatic identification without candidate comparison." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "targeted_msms", "unspecified" ], "ion_mobility": "not_applicable" }, "description": "MassFormer predicts small-molecule tandem mass spectra from molecular structures with a graph-transformer model. The research implementation includes inference scripts, pretrained checkpoints and training/evaluation configurations; prediction takes candidate structures rather than an unknown experimental spectrum alone." }, { "$schema": "../../schemas/tool.schema.json", "slug": "massistant", "name": "MASSISTANT", "summary": "MASSISTANT provides research code for learning molecular SELFIES representations from electron-ionization mass spectra.", "links": { "primary": "https://github.com/BartaLazar/MASSISTANT", "repository": "https://github.com/BartaLazar/MASSISTANT", "documentation": "https://github.com/BartaLazar/MASSISTANT#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "gc_ms" ], "interfaces": [ "notebook" ], "access": { "model": "source_available", "notes": "Public research source, no repository-wide license grant established, and final NIST-trained models are not supplied. Users must obtain suitable data rights and train their own model." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures" ], "other_input_formats": [ "Prepared EI spectrum/molecular-structure datasets" ], "other_output_formats": [ "Featurized training tables and trained SELFIES-prediction models" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-07-11", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MASSISTANT provides research code for learning molecular SELFIES representations from electron-ionization mass spectra. Its documented workflow covers dataset cleaning, SELFIES featurization and model training for de novo structure prediction.", "scope": { "designed_for": [ "MASSISTANT provides research code for learning molecular SELFIES representations from electron-ionization mass spectra." ], "not_designed_for": [ "Immediate inference with a distributed final model; the README still marks prediction instructions and a Docker image as forthcoming." ], "requirements": [ "A compatible research environment and appropriately licensed EI training spectra; the repository does not supply the commercial-NIST-trained models." ], "considerations": [ "This is a training-oriented research release. Valid SELFIES syntax does not establish that a predicted molecule is the experimentally measured compound." ] }, "common_uses": [ "MASSISTANT provides research code for learning molecular SELFIES representations from electron-ionization mass spectra." ], "publications": [ { "doi": "10.1016/j.chroma.2025.466216", "type": "primary", "citation": "MASSISTANT: A deep learning model for De Novo molecular structure prediction from EI‑MS spectra via SELFIES encoding. Journal of Chromatography A (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "massive", "name": "MassIVE", "summary": "A public repository for depositing, preserving, browsing, downloading, and reusing mass-spectrometry datasets, including datasets connected to GNPS analyses.", "description": "MassIVE is a community repository for the public exchange of mass-spectrometry data. It stores raw and processed files, metadata and publication-associated datasets, and supports browsing, downloading, accessioned deposition, and reuse through GNPS and related services.", "links": { "primary": "https://massive.ucsd.edu/", "homepage": "https://massive.ucsd.edu/", "documentation": "https://ccms-ucsd.github.io/GNPSDocumentation/datasets/" }, "resource_types": [ "data_repository" ], "functions": { "primary": "data_management_sharing", "secondary": [ "reference_data_search", "workflow_reproducibility" ], "capabilities": [ "repository_scale_search", "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "targeted", "untargeted", "metabolite_profiling", "lipidomics" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "technical": { "operating_systems": [ "browser" ], "api_available": false }, "data": { "input_types": [ "raw_instrument_data", "spectra", "feature_table", "sample_metadata" ], "output_types": [ "raw_instrument_data", "spectra", "feature_table", "sample_metadata" ], "input_formats": [ "vendor_raw", "mzml", "mzxml", "mgf", "csv_tsv" ], "output_formats": [ "vendor_raw", "mzml", "mzxml", "mgf", "csv_tsv" ] }, "publications": [], "common_uses": [ "Deposit publication-associated mass-spectrometry datasets.", "Browse and download public mass-spectrometry data for reuse and reanalysis.", "Deposit a publication-associated dataset and provide its accession so others can download and reanalyze it." ], "scope": { "designed_for": [ "Long-term deposition and public reuse of mass-spectrometry datasets." ], "requirements": [ "Data and metadata must be uploaded and a dataset explicitly made public when public access is intended." ], "considerations": [ "Dataset-level metadata quality affects downstream discoverability and reanalysis.", "Keep raw data, processed files and a sample-to-file mapping together; public deposition alone does not imply that the data are analysis-ready." ], "not_designed_for": [ "Automatic validation of metabolite identities or a substitute for sample-level experimental documentation." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2", "msn" ], "strategies": [ "unspecified" ], "ion_mobility": "supported" }, "related_tools": [ { "slug": "gnps", "relationship": "integration" }, { "slug": "redu", "relationship": "integration" }, { "slug": "masst", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-21" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed. Repository guidance is based on official deposition documentation. No standalone metabolomics-specific MassIVE methods paper is assigned here; cite the accession and any analysis methods actually used." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-21", "updated_at": "2026-09-06", "last_verified": "2026-08-21", "last_editorial_review": "2026-09-06" }, "aliases": [ "Mass Spectrometry Interactive Virtual Environment" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "massnet", "name": "massNet", "summary": "Learn spectral features and classify tissue regions in mass-spectrometry images.", "description": "massNet combines learned spectral representations with supervised tissue classification and exploration of discriminating peaks.", "links": { "primary": "https://github.com/wabdelmoula/massNet#readme", "repository": "https://github.com/wabdelmoula/massNet", "documentation": "https://github.com/wabdelmoula/massNet#readme" }, "platforms": [ "imaging_ms" ], "interfaces": [ "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "machine_learning", "capabilities": [ "feature_selection" ] }, "access": { "model": "open_source", "license": "3D Slicer Software License", "notes": "The repository supplies the 3D Slicer Software License agreement. This is research software; no installation or performance validation was performed." }, "data": { "input_types": [ "imaging_data", "sample_metadata" ], "output_types": [ "imaging_data", "statistical_results" ], "other_input_formats": [ "HDF5 spectra, m/z, tissue labels and pixel coordinates" ], "other_output_formats": [ "Classification probabilities, maps, learned features and plots" ] }, "common_uses": [ "Learn spectral features and classify tissue regions in mass-spectrometry images." ], "scope": { "designed_for": [ "massNet combines learned spectral representations with supervised tissue classification and exploration of discriminating peaks." ], "not_designed_for": [ "A ready-to-use clinical diagnostic or direct raw-vendor-file importer." ], "requirements": [ "Prepared arrays matching Main.py; labels for supervised training and evaluation.", "Legacy Python 3.6, Keras 2.2 and TensorFlow 1.8 environment documented by the authors; adaptation may be needed." ], "considerations": [ "The published example concerns tumor-bearing mouse-brain MSI; assess transfer to other tissues and instruments.", "Keep independent specimens separate during evaluation to avoid leakage between correlated pixels." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btac032", "type": "primary", "citation": "massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation. Bioinformatics (2022)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "analysis_types": [ "spatial_metabolomics" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "massql-playground", "name": "MassQL Playground", "summary": "Browser-based MassQL query editor and runner for exploring fragmentation patterns in local mass-spectrometry files.", "description": "MassQL Playground parses queries, explains their structure and runs them against local data in the browser. It provides a lightweight way to develop a spectral query before using the same language in larger analyses.", "links": { "primary": "https://apps.gnps2.org/massql_playground", "documentation": "https://apps.gnps2.org/massql_playground" }, "resource_types": [ "web_service" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "ms2_processing" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public browser app with local file processing. Hosted availability does not establish the license of the underlying engine or reference data." }, "data": { "input_types": [ "spectra" ], "input_formats": [ "mzml", "mgf", "json" ], "other_input_formats": [ "MassQL query text" ], "output_types": [ "report" ], "output_formats": [ "csv_tsv", "json" ] }, "common_uses": [ "Develop and inspect fragmentation-pattern queries.", "Search a local spectral file and export matching rows." ], "scope": { "designed_for": [ "Interactive MassQL query development and local spectral searching." ], "not_designed_for": [ "Raw feature detection or definitive molecular identification from a query match." ], "requirements": [ "A modern browser and a supported local spectral file for execution." ], "considerations": [ "The app reads whole files into browser memory; large files may exceed available resources.", "Displayed rows are capped even when the reported match count is larger.", "Retain the query and input-file identity to make results reproducible." ] }, "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Listed among GNPS2 Next Gen Web Apps." }, { "slug": "massql", "relationship": "integration", "note": "Runs the MassQL query engine." }, { "slug": "gnps-massql-analysis", "relationship": "alternative", "note": "Hosted library-query interface with LLM assistance, rather than local browser-file execution." } ], "publications": [], "resource_citations": [ { "citation": "GNPS2 Next Gen Web Apps. MassQL Playground interface and usage notes. Accessed 11 September 2026.", "type": "documentation", "url": "https://apps.gnps2.org/massql_playground" } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official app page and GNPS2 app index reviewed on 2026-09-11. No standalone publication for this interface verified; the canonical MassQL entry holds method publications. No file processing or independent benchmark performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-11", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "massql", "name": "MassQL", "summary": "An open-source, instrument-independent query language and software ecosystem for searching mass-spectrometry data using reusable MS1, MS/MS, chromatographic, and ion-mobility patterns.", "links": { "primary": "https://mwang87.github.io/MassQueryLanguage_Documentation/", "documentation": "https://mwang87.github.io/MassQueryLanguage_Documentation/", "repository": "https://github.com/mwang87/MassQueryLanguage", "web_app": "https://massql.gnps2.org/", "tutorial": "https://mwang87.github.io/MassQueryLanguage_Documentation/" }, "resource_types": [ "programming_package", "api_service", "web_service" ], "functions": { "primary": "spectral_analysis", "secondary": [ "reference_data_search", "data_management_sharing", "workflow_reproducibility" ], "capabilities": [ "repository_scale_search", "ms2_processing", "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "targeted", "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app", "command_line", "python_package", "api", "workflow_language" ], "access": { "model": "open_source", "license": "MIT", "notes": "The software license applies to the code; separately acquired libraries, vendor readers and hosted services may have their own terms." }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "programming_languages": [ "Python" ], "package_managers": [ "PyPI" ], "api_available": true }, "data": { "input_types": [ "spectra", "raw_instrument_data" ], "output_types": [ "spectra", "feature_table", "report" ], "input_formats": [ "mzml", "mzxml", "mgf" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1038/s41592-025-02660-z", "citation": "Damiani T, Jarmusch AK, Aron AT, et al. A universal language for finding mass spectrometry data patterns. Nat Methods 22, 1247-1254 (2025).", "type": "primary" } ], "common_uses": [ "Encode reusable searches for MS1 isotopic/adduct patterns, MS/MS fragments, neutral losses, mass defects, and combinations of these constraints.", "Combine spectral constraints with retention-time and ion-mobility conditions and Boolean logic.", "Run the same conceptual query in local files, software implementations, or repository-scale workflows without writing a bespoke analysis script.", "Reuse a chemical-pattern query across compatible engines while checking their supported syntax." ], "related_tools": [ { "slug": "gnps", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "Flexible, human-readable interrogation of mass-spectrometry data using chemically or biologically motivated signal patterns." ], "requirements": [ "A MassQL-compatible engine or application and mass-spectrometry data in a supported format." ], "considerations": [ "MassQL retrieves spectra/scans that satisfy user-defined constraints; the specificity and biological interpretation depend on the query design.", "Different software packages implement the shared MassQL grammar with their own optimized query engines, so supported syntax should be checked against the implementation in use.", "Store the exact query and engine version; inspect matched and unmatched examples to assess whether the intended chemistry is captured." ], "not_designed_for": [ "Proving an exact structure merely because a spectrum satisfies a diagnostic-fragment query." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia", "targeted_msms", "unspecified" ], "ion_mobility": "supported" }, "aliases": [ "Mass Query Language" ], "description": "MassQL (Mass Spectrometry Query Language) is a domain-specific language for expressing chemically meaningful patterns directly in mass-spectrometry data. Queries can combine precursor and fragment masses, neutral-loss-like constraints, intensity rules, Boolean logic, retention-time constraints, and ion-mobility constraints. The ecosystem includes a reference Python engine, web interfaces, repository-scale workflows, a Nextflow implementation, APIs, a community query compendium, and native implementations in multiple metabolomics software platforms." }, { "$schema": "../../schemas/tool.schema.json", "slug": "massqlab", "name": "MassQLab", "summary": "MassQLab applies named MassQL queries across a directory of mass-spectrometry data and summarizes results as tables and figures.", "links": { "primary": "https://github.com/JohnsonDylan/MassQLab", "repository": "https://github.com/JohnsonDylan/MassQLab", "documentation": "https://github.com/JohnsonDylan/MassQLab#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "spectral_analysis", "secondary": [ "quality_control", "visualization_reporting" ], "capabilities": [ "ms2_processing" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "command_line", "desktop_gui", "notebook" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "mzML files, MassQL query tables and JSON configuration" ], "other_output_formats": [ "Query/peak-analysis CSV tables and summary figures" ] }, "related_tools": [ { "slug": "massql", "relationship": "dependency" } ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-05-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MassQLab applies named MassQL queries across a directory of mass-spectrometry data and summarizes results as tables and figures. It supports system-suitability and recurring MS1/MS2 query workflows through scripts, notebooks and an evolving GUI.", "scope": { "designed_for": [ "MassQLab applies named MassQL queries across a directory of mass-spectrometry data and summarizes results as tables and figures." ], "not_designed_for": [ "Automatic selection of scientifically meaningful queries or universal metabolite identification from a matched mass pattern." ], "requirements": [ "Python dependencies, mzML files and named queries in JSON/CSV/XLSX with project configuration; optional vendor conversion requires an installed MSConvert executable." ], "considerations": [ "The configured directory is processed as a whole. Review query tolerances, peak fitting and cache settings; standalone GUI development and documented background-process behavior affect use." ] }, "common_uses": [ "MassQLab applies named MassQL queries across a directory of mass-spectrometry data and summarizes results as tables and figures." ], "publications": [ { "doi": "10.1002/rcm.10132", "type": "primary", "citation": "Open‐Source System Suitability: Mass Spectrometry Query Language Lab (MassQLab). Rapid Communications in Mass Spectrometry (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "masst", "name": "MASST", "acronym": "MASST", "summary": "A GNPS repository-scale spectral search service for finding where a query MS/MS spectrum or related spectra occur across public metabolomics data and spectral libraries.", "description": "MASST (Mass Spectrometry Search Tool) searches a query tandem mass spectrum against public metabolomics data and spectral libraries to locate matching or related spectra and the files in which they occur. The current GNPS2 implementation uses accelerated indexing and supports current pan-repository metabolomics indexes, programmatic API access, command-line access, and GNPS2 workflow execution.", "links": { "primary": "https://masst.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/masst/", "web_app": "https://masst.gnps2.org/", "repository": "https://github.com/CCMS-UCSD/GNPS_Workflows/tree/master/search_single_spectrum" }, "resource_types": [ "web_service" ], "functions": { "primary": "reference_data_search", "secondary": [ "spectral_analysis", "data_management_sharing" ], "capabilities": [ "library_matching", "metadata_contextualization", "repository_scale_search" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app", "api" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "technical": { "operating_systems": [ "browser" ], "api_available": true }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra", "sample_metadata", "report" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "Universal Spectrum Identifier (USI) or peak list" ] }, "publications": [ { "doi": "10.1038/s41587-019-0375-9", "citation": "Wang M, Jarmusch AK, Vargas F, et al. Mass spectrometry searches using MASST. Nat Biotechnol 38, 23-26 (2020).", "type": "primary" } ], "common_uses": [ "Search a query MS/MS spectrum across public metabolomics datasets and spectral libraries.", "Identify datasets, files, or sample contexts in which a molecule or close spectral analogue may occur.", "Use repository-scale matches as a starting point for biological, environmental, natural-product, or reverse-metabolomics hypotheses.", "Locate public datasets containing spectra similar to an unknown feature for follow-up reanalysis." ], "scope": { "designed_for": [ "Repository-scale contextualization of individual MS/MS spectra." ], "requirements": [ "A centroided query MS/MS spectrum with precursor information." ], "considerations": [ "Search tolerances, cosine/similarity thresholds, and repository composition affect which matches are returned.", "A spectral match indicates MS/MS similarity and possible occurrence; it is not by itself definitive structural confirmation.", "The current GNPS2 implementation is substantially faster than the original MASST workflow and also supports programmatic access described in the current documentation.", "Current pan-repository indexes extend beyond the original GNPS/MassIVE-only implementation; the exact indexed collections can change over time.", "Retain the query spectrum identifier and inspect matched files and their study metadata before drawing an occurrence conclusion." ], "not_designed_for": [ "Quantitative comparison across unrelated public studies or proof of biosynthetic origin." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "redu", "relationship": "integration" }, { "slug": "massive", "relationship": "integration" }, { "slug": "fasst", "relationship": "uses", "note": "Current GNPS2 MASST searching uses accelerated indexed search infrastructure." }, { "slug": "structuremasst", "relationship": "complementary", "note": "StructureMASST generalizes repository searching from a single reference spectrum to chemical structure or substructure queries." } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-21", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "aliases": [ "Mass Spectrometry Search Tool" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "matchms", "name": "matchms", "summary": "Build Python workflows for spectral cleaning, metadata harmonization and MS/MS similarity scoring.", "description": "matchms provides composable processing filters and similarity functions for spectral collections, supporting reproducible comparison and downstream library-search workflows.", "links": { "primary": "https://matchms.readthedocs.io/en/latest/", "documentation": "https://matchms.readthedocs.io/en/latest/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "library_matching", "ms2_processing" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "notes": "Open-source software; use the official installation, license and citation guidance for the selected release." }, "data": { "input_types": [ "spectra", "spectral_library" ], "output_types": [ "spectra", "statistical_results" ], "input_formats": [ "mgf", "msp" ], "output_formats": [ "other" ], "other_output_formats": [ "Spectra, similarity scores and pipeline reports" ] }, "publications": [ { "citation": "Huber, Verhoeven, Meijer, et al. matchms - processing and similarity evaluation of mass spectrometry data. Journal of Open Source Software 5, 2411 (2020).", "type": "primary", "doi": "10.21105/joss.02411" } ], "common_uses": [ "Harmonize spectral metadata before comparison.", "Calculate similarity scores between query and reference spectra." ], "scope": { "designed_for": [ "Harmonize spectral metadata before comparison." ], "not_designed_for": [ "Equating a similarity score with an independently confirmed structure." ], "requirements": [ "Python, supported spectral data and an explicitly configured preprocessing pipeline." ], "considerations": [ "Precursor metadata, mass tolerance and peak filtering influence scores; retain the settings used for comparisons." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "spec2vec", "relationship": "complementary" }, { "slug": "ms2deepscore", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "matrixqcvis", "name": "MatrixQCvis", "summary": "Explore sample and feature quality in metabolomics matrices through interactive visualizations.", "description": "MatrixQCvis is a Bioconductor/Shiny interface for inspecting quantitative matrices. It visualizes distributions, sample relationships, outliers and batch structure, using SummarizedExperiment as its input container.", "links": { "primary": "https://bioconductor.org/packages/MatrixQCvis/", "repository": "https://github.com/tnaake/MatrixQCvis", "documentation": "https://bioconductor.org/packages/MatrixQCvis/" }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package", "web_app" ], "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "capabilities": [ "interactive_dashboard" ] }, "access": { "model": "open_source", "license": "GPL-3.0", "notes": "License checked against official package metadata or repository. Local setup requires the documented dependencies; no installation or hosted-service availability test was performed." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "report" ], "other_input_formats": [ "SummarizedExperiment object supplied directly or as an RDS file" ], "other_output_formats": [ "Interactive per-sample and per-feature quality visualizations" ] }, "common_uses": [ "Inspect processed MS-metabolomics matrices before interpretation." ], "scope": { "designed_for": [ "Explore sample and feature quality in metabolomics matrices through interactive visualizations." ], "not_designed_for": [ "Confirming compound identity or replacing study design and scientific review." ], "requirements": [ "A feature-by-sample matrix and metadata prepared as a SummarizedExperiment object." ], "considerations": [ "Visual separation alone cannot determine whether a batch difference is technical or biological.", "Use the compatible Bioconductor release rather than assuming the development branch is production-ready." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btab748", "type": "primary", "citation": "MatrixQCvis: shiny-based interactive data quality exploration for omics data. Bioinformatics (2021).", "note": "Published online in 2021; assigned to the 2022 journal issue. Replaces the intake preprint citation." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate verified against official documentation, source/package metadata and primary-publication DOI/title/year on 2026-10-02. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "maw", "name": "MAW", "summary": "A reproducible metabolome-annotation workflow combining spectral-database dereplication and compound-database annotation from tandem mass-spectrometry data.", "links": { "primary": "https://github.com/zmahnoor14/MAW", "repository": "https://github.com/zmahnoor14/MAW", "documentation": "https://github.com/zmahnoor14/MAW#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "annotation_identification", "spectral_analysis" ], "capabilities": [ "library_matching", "structure_database_search", "provenance_tracking" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "r_package", "command_line" ], "access": { "model": "source_available", "notes": "Public code with an MIT-labelled README badge, but no standard repository-wide license file was identified. External software, libraries and SIRIUS services retain separate licenses/access requirements." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R", "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "other_input_formats": [ "MS2 mzML files, reference-library RData files and workflow configuration" ], "other_output_formats": [ "Candidate annotation tables and intermediate R/Python workflow artifacts" ] }, "maintenance": { "status": "limited", "repository_last_activity": "2025-10-31", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MAW combines spectral-library dereplication and in-silico candidate ranking in an executable tandem-MS annotation workflow. R and Python stages connect reference libraries with SIRIUS or MetFrag and produce tentative candidate/class annotations with intermediate provenance.", "scope": { "designed_for": [ "A reproducible metabolome-annotation workflow combining spectral-database dereplication and compound-database annotation from tandem mass-spectrometry data." ], "not_designed_for": [ "Raw chromatographic feature extraction or a guarantee that consensus candidate selection confirms molecular identity." ], "requirements": [ "Supported mzML MS2 data, reference-library files and the configured R/Python/container environment; CWL and Docker routes have different SIRIUS/MetFrag capabilities and external access needs." ], "considerations": [ "The README marks the workflow as not maintained. Check older dependency and database compatibility; CWL documentation specifies the MetFrag route while SIRIUS is documented through separate container steps." ] }, "common_uses": [ "A reproducible metabolome-annotation workflow combining spectral-database dereplication and compound-database annotation from tandem mass-spectrometry data." ], "publications": [ { "doi": "10.1186/s13321-023-00695-y", "type": "primary", "citation": "MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry. Journal of Cheminformatics (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mb-post", "name": "MB-POST", "aliases": [], "summary": "MB-POST is a metabolomics mass spectrometry data repository which implements a reanalysis-oriented metadata framework that captures the information essential for data reuse. It provides a streamlined submission system that enables the rapid deposition of standardized metabolomics metadata together with experimental data.", "links": { "primary": "https://repository.massbank.jp/", "documentation": "https://repository.massbank.jp/help#before-submission", "homepage": "https://repository.massbank.jp/" }, "resource_types": [ "data_repository" ], "functions": { "primary": "data_management_sharing", "secondary": [ "reference_data_search" ], "capabilities": [] }, "platforms": [ "multi_platform" ], "analysis_types": [], "interfaces": [ "web_app" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.6c00697", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-23" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/52", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389", "notes": "MB-POST is a mass spectrometry data repository for metabolomics. It consists of a high-speed file upload process, flexible file management system and easy-to-use interfaces. \nUsers can release their \"raw/processed\" data via this site with a unique identifier number for the paper publication. Users also can suspend (or \"embargo\") their data until their paper is published. \nThe file transfer from users' computer to our repository server is very fast and uses only web browsers – it does not require installing any additional software." }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mbrole", "name": "MBROLE3", "summary": "Test functional enrichment in lists of annotated metabolites.", "description": "MBROLE3 tests overrepresentation of chemical and functional annotations, with identifier conversion and separate direct and indirect annotation results.", "links": { "primary": "https://csbg.cnb.csic.es/mbrole3/", "documentation": "https://csbg.cnb.csic.es/mbrole3/help.php" }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "resource_types": [ "software_application" ], "functions": { "primary": "pathway_interpretation", "capabilities": [ "pathway_enrichment" ] }, "access": { "model": "free_web_service", "notes": "The primary paper describes a free service without login. No software license or service execution validation is asserted." }, "data": { "input_types": [ "compound_list" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Compound IDs separated by tabs or newlines; optional background list" ], "other_output_formats": [ "Enrichment tables, TSV download and dot/bar plots" ] }, "common_uses": [ "Test functional enrichment in lists of annotated metabolites." ], "scope": { "designed_for": [ "MBROLE3 tests overrepresentation of chemical and functional annotations, with identifier conversion and separate direct and indirect annotation results." ], "not_designed_for": [ "Identification of unknown spectra or direct testing of unannotated m/z features." ], "requirements": [ "Mapped compound identifiers, selected annotation vocabularies and a suitable background set." ], "considerations": [ "Use a background reflecting measurable compounds. Indirect literature/protein associations are not direct experimental evidence of pathway activity." ] }, "publications": [ { "doi": "10.1093/nar/gkad405", "type": "primary", "citation": "MBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis. Nucleic Acids Research (2023)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "aliases": [ "MBROLE", "MBROLE 3" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mergeion", "name": "MergeION", "summary": "A workflow for extracting and merging LC-MS/MS spectra into local GNPS-style spectral libraries with user metadata, plus local and public-library searching for annotation.", "links": { "primary": "https://github.com/daniellyz/meRgeION2", "repository": "https://github.com/daniellyz/meRgeION2", "documentation": "https://daniellyz.github.io/mergeion.github.io/Library_Generation_Tutorial.html" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "spectral_analysis", "secondary": [ "annotation_identification", "data_management_sharing" ], "capabilities": [ "reference_spectrum_selection", "library_matching", "spectral_data_io" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "unknown", "notes": "Package DESCRIPTION says only GNU without a version or complete grant; no precise unrestricted license is inferred. Linked reference libraries have separate provenance and reuse terms." }, "data": { "input_types": [ "raw_instrument_data", "spectra", "sample_metadata" ], "output_types": [ "spectral_library", "compound_list" ], "other_input_formats": [ "mzML/mzXML scans plus targets/metadata, or two-column query spectra" ], "other_output_formats": [ "R spectral-library objects, candidate match tables and mirror/network plots" ] }, "common_uses": [ "Build local LC-MS/MS spectral libraries without publishing confidential metadata.", "Search unknown spectra against local or public reference libraries." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-22", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda", "targeted_msms" ], "ion_mobility": "unspecified" }, "description": "MeRgeION builds local spectral libraries by extracting and combining MS scans around user-specified compounds. Its R functions also support local spectral searching, analog analysis and network-oriented exploration without requiring public deposition of the library.", "scope": { "designed_for": [ "A workflow for extracting and merging LC-MS/MS spectra into local GNPS-style spectral libraries with user metadata, plus local and public-library searching for annotation." ], "not_designed_for": [ "Reference-free structure determination or permission to redistribute third-party spectra included in a local library." ], "requirements": [ "R/Bioconductor dependencies and Java/OpenBabel components as documented; compatible mzML/mzXML DDA or targeted-MS/MS data, target masses/optional retention times and library metadata." ], "considerations": [ "Library quality depends on extraction/merging settings and metadata. The linked example library is positive-ion; assess polarity and acquisition compatibility before transferring search thresholds." ] }, "publications": [ { "doi": "10.1021/acs.analchem.2c04343", "type": "primary", "citation": "MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing. Analytical Chemistry (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "messes", "name": "MESSES", "summary": "MESSES extracts tagged experimental metadata from spreadsheets or CSV into JSON, validates structured metadata against schemas and converts supported records to mwTab for Metabolomics Workbench deposition.", "links": { "primary": "https://github.com/MoseleyBioinformaticsLab/MESSES", "repository": "https://github.com/MoseleyBioinformaticsLab/MESSES", "documentation": "https://moseleybioinformaticslab.github.io/MESSES/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_management_sharing", "secondary": [ "data_conversion", "workflow_reproducibility" ], "capabilities": [ "metadata_contextualization", "provenance_tracking" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "source_available", "license": "Modified Clear BSD with citation requirement", "notes": "Repository LICENSE adds a publication-citation condition to Clear BSD and grants no patent rights. The custom text is identified explicitly rather than labelled standard BSD-3-Clause." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python", "Cython" ] }, "data": { "input_types": [ "sample_metadata" ], "output_types": [ "sample_metadata" ], "other_input_formats": [ "Tagged Excel/CSV metadata and extraction/schema directives" ], "other_output_formats": [ "Structured JSON metadata, validation messages and mwTab exports" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-06-23", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MESSES extracts tagged experimental metadata from spreadsheets or CSV into JSON, validates structured metadata against schemas and converts supported records to mwTab for Metabolomics Workbench deposition. Its command-line and Python interfaces separate extraction, validation and conversion.", "scope": { "designed_for": [ "MESSES extracts tagged experimental metadata from spreadsheets or CSV into JSON, validates structured metadata against schemas and converts supported records to mwTab for Metabolomics Workbench deposition." ], "not_designed_for": [ "Raw spectral processing or confirmation that scientifically incorrect metadata becomes correct merely because it passes a schema." ], "requirements": [ "Python dependencies, tagged source tables and the extraction directives and validation schemas appropriate to the experiment and destination repository." ], "considerations": [ "Tagging and schema setup require knowledge of the study. Review identifiers, units and repository-specific fields after extraction and conversion." ] }, "common_uses": [ "MESSES extracts tagged experimental metadata from spreadsheets or CSV into JSON, validates structured metadata against schemas and converts supported records to mwTab for Metabolomics Workbench deposition." ], "publications": [ { "doi": "10.3390/metabo13070842", "type": "primary", "citation": "MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing. Metabolites (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "met4dx", "name": "Met4DX", "summary": "Process LC-ion-mobility-MS data across supported four-dimensional acquisition modes.", "description": "Met4DX provides R workflows for feature detection, MS/MS generation and annotation in supported PASEF-DDA, PASEF-DIA and IM-AIF datasets.", "links": { "primary": "https://github.com/ZhuMetLab/Met4DX", "repository": "https://github.com/ZhuMetLab/Met4DX", "documentation": "https://github.com/ZhuMetLab/Met4DX#readme" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "capabilities": [ "peak_detection", "ms2_processing" ] }, "access": { "model": "source_available", "notes": "Repository LICENCE specifies CC BY-NC-ND 4.0, including noncommercial and no-derivatives restrictions. Public availability is not an unrestricted open-source license.", "license": "CC-BY-NC-ND-4.0" }, "data": { "input_types": [ "raw_instrument_data", "compound_list" ], "output_types": [ "feature_table", "spectra" ], "other_input_formats": [ "Instrument-specific .d data or converted IM-AIF mzML, with mode-specific tables" ], "other_output_formats": [ "Processed feature tables and generated MS/MS spectra" ] }, "common_uses": [ "Process LC-ion-mobility-MS data across supported four-dimensional acquisition modes." ], "scope": { "designed_for": [ "Met4DX provides R workflows for feature detection, MS/MS generation and annotation in supported PASEF-DDA, PASEF-DIA and IM-AIF datasets." ], "not_designed_for": [ "Universal processing of arbitrary ion-mobility instruments or unverified metabolite identification." ], "requirements": [ "Supported timsTOF or Agilent DTIM-MS acquisition and the documented R/Docker environment.", "Precursor lists for list-driven modes; CCS calibration coefficients for IM-AIF." ], "considerations": [ "Annotation-library and retention-time calibration requirements depend on the workflow selected." ] }, "publications": [ { "doi": "10.1021/jasms.4c00290", "type": "primary", "citation": "Met4DX: A Unified and Versatile Data Processing Tool for Multidimensional Untargeted Metabolomics Data. Journal of the American Society for Mass Spectrometry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboanalyst", "name": "MetaboAnalyst", "summary": "A web platform and associated R package for metabolomics data processing, statistical analysis, visualization, functional interpretation, and multi-omics integration.", "links": { "primary": "https://www.metaboanalyst.ca/", "web_app": "https://www.metaboanalyst.ca/", "documentation": "https://www.metaboanalyst.ca/docs/Tutorials.xhtml", "repository": "https://github.com/xia-lab/MetaboAnalystR", "tutorial": "https://www.metaboanalyst.ca/docs/RTutorial.xhtml" }, "resource_types": [ "web_service", "programming_package" ], "functions": { "primary": "statistics", "secondary": [ "normalization", "visualization_reporting", "pathway_interpretation", "multiomics_integration", "preprocessing" ], "capabilities": [ "univariate_statistics", "multivariate_statistics", "feature_selection", "pathway_enrichment", "interactive_dashboard" ] }, "platforms": [ "multi_platform", "platform_independent" ], "analysis_types": [ "targeted", "untargeted", "metabolite_profiling", "lipidomics", "multiomics" ], "interfaces": [ "web_app", "r_package", "api" ], "access": { "model": "free_web_service", "notes": "A public web service is available. The project also offers commercial Pro deployments and training; MetaboAnalystR has its own installation and licensing information." }, "technical": { "operating_systems": [ "browser", "cross_platform" ], "programming_languages": [ "R" ], "api_available": true }, "data": { "input_types": [ "feature_table", "compound_list", "sample_metadata", "statistical_results", "spectra" ], "output_types": [ "statistical_results", "pathway_data", "report" ], "input_formats": [ "csv_tsv", "mzml", "mzxml", "msp" ], "output_formats": [ "csv_tsv" ], "other_output_formats": [ "PNG", "PDF", "SVG" ] }, "publications": [ { "doi": "10.1093/nar/gkab382", "type": "methods", "citation": "Pang Z, Chong J, Zhou G, et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 49, W388-W396 (2021)." }, { "doi": "10.1093/nar/gkae253", "citation": "Pang Z, Lu Y, Zhou G, et al. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 52, W398-W406 (2024).", "type": "primary" } ], "common_uses": [ "Perform exploratory and inferential statistics on metabolomics data tables.", "Generate visualizations and pathway-level interpretations.", "Integrate metabolomics with transcriptomics or other omics results.", "Save result tables and the analysis settings so statistical and pathway results can be reconstructed." ], "related_tools": [], "maintenance": { "status": "active", "last_checked": "2026-08-04" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-21", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "Exploratory and inferential analysis of metabolomics abundance tables with a defined study design.", "Pathway and metabolite-set analysis using annotated compounds or supported untargeted peak lists." ], "not_designed_for": [ "Treating a predictive model or pathway association as experimentally established biological causation." ], "requirements": [ "Prepare sample labels and covariates consistently with the selected statistical module.", "For pathway analysis, inspect identifier mapping and choose the relevant organism and background; raw-data modules have separate input requirements." ], "considerations": [ "Report missing-value handling, normalization, transformation, scaling and multiple-testing choices.", "Evaluate supervised models with appropriate validation; a separated score plot alone is insufficient evidence of predictive performance.", "The public web service and optional commercial services have different limits and deployment options.", "Check the number of compounds that map to the selected pathway resource and report the background used for enrichment." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda", "dia" ], "ion_mobility": "not_required" }, "description": "MetaboAnalyst combines web modules for statistical analysis, functional interpretation and integration of metabolomics results. Researchers can start with abundance tables and sample metadata, annotated metabolite lists, or supported LC-MS spectra. MetaboAnalyst 6.0 includes MS/MS annotation, analysis with covariates, dose-response analysis and genetic association-based causal analysis. MetaboAnalystR supports scripted workflows. Inputs and outputs depend on the module, so users should identify the analysis performed rather than report only the platform name." }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboanalystr", "name": "MetaboAnalystR", "summary": "Run metabolomics processing, statistics and functional interpretation through R.", "description": "MetaboAnalystR provides a programmable counterpart to the MetaboAnalyst ecosystem. Version 4 integrates LC-MS processing, annotation and downstream analysis.", "links": { "primary": "https://github.com/xia-lab/MetaboAnalystR", "documentation": "https://github.com/xia-lab/MetaboAnalystR", "repository": "https://github.com/xia-lab/MetaboAnalystR" }, "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "capabilities": [ "univariate_statistics", "multivariate_statistics" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Open-source R framework; external databases and services have separate terms." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "R matrices and module-specific input files" ], "other_output_formats": [ "R analysis objects, tables and figures" ] }, "publications": [ { "citation": "Pang Z, Xu L, Viau C, et al. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nat Commun 15, 3675 (2024).", "type": "primary", "doi": "10.1038/s41467-024-48009-6" } ], "common_uses": [ "Reproduce and extend analyses in R scripts.", "Connect processed metabolite tables to statistical and pathway workflows." ], "scope": { "designed_for": [ "Reproduce and extend analyses in R scripts." ], "not_designed_for": [ "Replacing experimental validation of biomarkers or pathway mechanisms." ], "requirements": [ "R and module-specific dependencies; raw-data workflows need additional computing and reference resources." ], "considerations": [ "Keep package versions, parameters and database releases with the analysis. Local and web implementations can differ." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metaboanalyst", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboannoate", "name": "MetaboAnnoate", "aliases": [], "summary": "MetaboAnnotate is an AI-powered system for metabolite annotation of MS/MS data. \nAgents (Supervisor, Planner, Executor, Evaluator) orchestrate workflows and call external tools.\nThe goal of MetabAnnotate is to streamline complex annotation workflows in metabolomics using a modular AI-agent framework.", "links": { "primary": "https://hassounlab.cs.tufts.edu/MetaboAnnotate/", "homepage": "https://hassounlab.cs.tufts.edu/MetaboAnnotate/" }, "resource_types": [ "web_service" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "web_app" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.6c01717", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-18" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/41", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-18", "updated_at": "2026-09-18", "last_verified": "2026-09-18" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboannotation", "name": "MetaboAnnotation", "summary": "Match metabolomics features and spectra against reference values through a unified R interface.", "description": "MetaboAnnotation supports mass matching, matching with retention times and spectral-library comparisons, with data objects that retain query-reference relationships.", "links": { "primary": "https://bioconductor.org/packages/MetaboAnnotation/", "documentation": "https://bioconductor.org/packages/MetaboAnnotation/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "library_matching", "structure_database_search" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Artistic-2.0 package; reference-data licensing is separate." }, "data": { "input_types": [ "feature_table", "spectra", "spectral_library" ], "output_types": [ "compound_list" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "R feature tables, Spectra and reference objects" ], "other_output_formats": [ "Matched query-reference objects" ] }, "publications": [], "resource_citations": [ { "citation": "MetaboAnnotation developers. MetaboAnnotation: Utilities for Annotation of Metabolomics Data. Bioconductor package record.", "type": "software", "doi": "10.18129/B9.bioc.MetaboAnnotation" } ], "common_uses": [ "Create tentative annotations from reference masses.", "Manage spectral matches within a Spectra-based workflow." ], "scope": { "designed_for": [ "Create tentative annotations from reference masses." ], "not_designed_for": [ "Guaranteeing structural identity from a mass match." ], "requirements": [ "R/Bioconductor, query features or spectra, and suitable reference data." ], "considerations": [ "Use retention information only when analytically comparable; report matching thresholds and evidence levels." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "spectra", "relationship": "complementary" }, { "slug": "metid", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboannotator", "name": "MetaboAnnotatoR", "summary": "An R package for annotating LC-MS all-ion-fragmentation metabolomics features using fragment-ion databases and centroided chromatographic data.", "links": { "primary": "https://github.com/gggraca/MetaboAnnotatoR", "repository": "https://github.com/gggraca/MetaboAnnotatoR", "documentation": "https://github.com/gggraca/MetaboAnnotatoR/tree/devel/vignettes" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "spectral_analysis" ], "capabilities": [ "library_matching", "ms2_processing" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra", "spectral_library" ], "output_types": [ "compound_list" ], "other_input_formats": [ "Centroided LC-MS AIF data and R feature/group/fragment-database objects" ], "other_output_formats": [ "R candidate annotations and feature-to-fragment evidence" ] }, "publications": [ { "doi": "10.1021/acs.analchem.1c03032", "type": "primary", "citation": "Automated Annotation of Untargeted All-Ion Fragmentation LC–MS Metabolomics Data with MetaboAnnotatoR. Analytical Chemistry (2022)." } ], "common_uses": [ "Annotate AIF LC-MS features by matching fragment-ion evidence to reference databases." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-09-11", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "all_ion" ], "ion_mobility": "not_required" }, "description": "MetaboAnnotatoR annotates LC-MS all-ion-fragmentation features using chromatographic information and metabolite fragment databases. The R workflow links processed AIF data to candidate metabolites and includes guidance for constructing fragment-database entries.", "scope": { "designed_for": [ "An R package for annotating LC-MS all-ion-fragmentation metabolomics features using fragment-ion databases and centroided chromatographic data." ], "not_designed_for": [ "Treating all coeluting fragments as compound-specific evidence or direct transfer of the AIF workflow to every DDA/DIA design." ], "requirements": [ "R/Bioconductor dependencies, centroided AIF chromatograms, compatible processed feature/group information and an appropriate ion-fragment database; follow the xcms/RAMClustR vignette for the documented example." ], "considerations": [ "Fragment coverage, chromatographic coelution and upstream grouping affect annotations. Candidate matches need assessment of competing explanations and additional evidence." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboapps", "name": "MetaboApps", "summary": "A modular collection of browser-based downstream analysis applications that extend GNPS2 results with targeted interpretation, statistics, metadata, and domain-specific readouts.", "description": "MetaboApps is a GNPS2 collection of lightweight downstream applications built to make specialized post-processing analyses accessible without bespoke scripting. The current collection includes applications for post-network MassQL, multi-step MassQL, drug and food readouts, CMMC analysis, FBMN statistics, chemical proportionality, CorrOmics, conjugated-metabolome exploration, and reverse-metabolomics-style analyses. Individual apps remain separate scientific modules, while this Navigator entry represents the maintained collection as a discovery and launch layer.", "links": { "primary": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapps_overview/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapps_overview/" }, "resource_types": [ "workflow_platform", "web_service" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "visualization_reporting", "spectral_analysis", "statistics", "reference_data_search" ], "capabilities": [ "interactive_dashboard", "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [], "common_uses": [ "Launch modular downstream analyses from GNPS2/molecular-networking outputs without writing custom scripts.", "Explore statistics, chemical pattern queries, drug/food annotations, microbial knowledge, and other specialized post-processing applications.", "Provide a discoverable collection for rapidly evolving community analysis modules while keeping core GNPS2 workflows separate.", "Apply an appropriate downstream app to a GNPS2 result and retain the underlying feature and sample identifiers." ], "scope": { "designed_for": [ "Post-processing and interpretation of GNPS2 and related metabolomics analysis outputs using modular web applications." ], "requirements": [ "Inputs vary by app and can include GNPS2 task identifiers, feature/network tables, spectra, or metadata." ], "considerations": [ "MetaboApps is a collection rather than one analytical algorithm; users should cite and document the specific app(s) used.", "Apps can evolve or be added more rapidly than peer-reviewed methodological publications.", "Choose the app first, then check its input contract and cited method; record its name, parameters and upstream task identifier." ], "not_designed_for": [ "One universal workflow with identical input formats, outputs or validation across every app." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "unspecified" ], "ion_mobility": "unspecified" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "massql", "relationship": "integration" }, { "slug": "cmmc-kb", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed. This is an application collection. Cite the selected application and its underlying method; no single collection-wide methods paper was identified in this pass." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-06", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser" ], "api_available": false }, "data": { "input_types": [ "feature_table", "spectra", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "csv_tsv" ], "other_input_formats": [ "GNPS2 task identifier (app dependent)" ], "other_output_formats": [ "Interactive results (app dependent)" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metabodiff", "name": "MetaboDiff", "summary": "Perform differential metabolomics analysis and explore metabolic correlation modules in R.", "description": "MetaboDiff starts from metabolite measurement tables and supports the analysis of sample traits through data-derived correlation networks.", "links": { "primary": "https://github.com/andreasmock/MetaboDiff", "documentation": "https://github.com/andreasmock/MetaboDiff", "repository": "https://github.com/andreasmock/MetaboDiff" }, "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "capabilities": [ "univariate_statistics", "multivariate_statistics" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "source_available", "notes": "Public R source; consult package metadata for licensing. Some older hosted tutorial links may require the repository vignettes instead." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Metabolite measurement and annotation tables" ], "other_output_formats": [ "R differential-analysis and network objects" ] }, "publications": [ { "citation": "Mock A, Warta R, Dettling S, et al. MetaboDiff: an R package for differential metabolomic analysis. Bioinformatics 34, 3417-3418 (2018).", "type": "primary", "doi": "10.1093/bioinformatics/bty344" } ], "common_uses": [ "Compare metabolite measurements between study groups.", "Explore modules associated with sample traits." ], "scope": { "designed_for": [ "Compare metabolite measurements between study groups." ], "not_designed_for": [ "Using correlations to establish biochemical reactions or causality." ], "requirements": [ "A metabolite abundance table and aligned sample/feature annotations." ], "considerations": [ "Network estimates depend on sample size, missing-data handling and covariates; investigate sensitivity before interpreting modules." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "ropls", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metabograph", "name": "MetaboGraph", "aliases": [], "summary": "MetaboGraph is a Framework for Metabolomics and Lipidomics Annotation and Pathway Network Analysis that provides end-to-end workflow for metabolite identification, pathway enrichment, net analysis, and multi-omics integration. Designed for both beginner and advanced users, MetaboGraph combines powerful computational methods with an intuitive graphical interface.", "links": { "primary": "https://github.com/odaramola92/MetaboGraph", "repository": "https://github.com/odaramola92/MetaboGraph" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "pathway_interpretation", "secondary": [ "reference_data_search", "visualization_reporting", "multiomics_integration" ], "capabilities": [] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "python_package" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.6c01446", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-23" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/50", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metabolights", "name": "MetaboLights", "summary": "Deposit and find metabolomics studies with experimental files and structured sample and assay metadata.", "description": "MetaboLights is the EMBL-EBI repository for metabolomics experiments across organisms and analytical techniques. Study records connect raw and derived files to sample and assay descriptions, supporting publication-associated sharing and later reuse.", "links": { "primary": "https://www.ebi.ac.uk/metabolights/", "homepage": "https://www.ebi.ac.uk/metabolights/", "documentation": "https://ebi-metabolights.github.io/guides/" }, "resource_types": [ "data_repository" ], "functions": { "primary": "data_management_sharing", "capabilities": [ "metadata_contextualization", "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public studies can be accessed freely. Submission uses an account and the repository submission and curation process." }, "data": { "input_types": [ "raw_instrument_data", "feature_table", "sample_metadata" ], "output_types": [ "raw_instrument_data", "feature_table", "sample_metadata" ], "input_formats": [ "isa_tab" ], "output_formats": [ "isa_tab" ], "other_input_formats": [ "Raw and derived assay files; accepted packaging depends on file type" ], "other_output_formats": [ "Submitted study files" ] }, "common_uses": [ "Deposit the files and metadata supporting a metabolomics publication.", "Find studies by organism or analytical technology for reanalysis." ], "scope": { "designed_for": [ "Structured sharing of complete metabolomics experiments, with this entry focused on MS-based studies." ], "not_designed_for": [ "Automatic harmonization of measurements across unrelated studies." ], "requirements": [ "Prepare sample-to-file mappings, assay details and metabolite identification tables following the submission guide." ], "considerations": [ "Inspect study design and processing history before combining datasets. Record the MTBLS accession and cite the original study as well as the repository." ] }, "publications": [ { "doi": "10.1093/nar/gkad1045", "citation": "Yurekten O, Payne T, Tejera N, et al. MetaboLights: open data repository for metabolomics. Nucleic Acids Res 52, D640-D646 (2024).", "type": "primary" } ], "related_tools": [ { "slug": "metabolomics-workbench", "relationship": "alternative" }, { "slug": "massive", "relationship": "alternative" } ], "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review on 7 September 2026 against official documentation and the sources listed here. Scope and practical guidance are editorial synthesis; software execution and service availability were not validated." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metabolomexchange", "name": "MetabolomeXchange", "summary": "Discover publicly released metabolomics studies across participating repositories.", "description": "MetabolomeXchange aggregates dataset announcements and metadata to help researchers locate studies; the underlying data remain with the originating repositories.", "links": { "primary": "https://leidenuniv-lacdr-abs.github.io/metabolomexchange/", "documentation": "https://leidenuniv-lacdr-abs.github.io/metabolomexchange/" }, "resource_types": [ "web_service" ], "functions": { "primary": "reference_data_search", "capabilities": [ "metadata_contextualization" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public dataset-discovery project. Source repository is linked from the official project page." }, "data": { "input_types": [ "sample_metadata" ], "output_types": [ "sample_metadata" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Study keywords and repository metadata" ], "other_output_formats": [ "Study announcements and source-repository links" ] }, "publications": [], "common_uses": [ "Find studies for secondary analysis.", "Follow links back to the repository that hosts a dataset." ], "scope": { "designed_for": [ "Find studies for secondary analysis." ], "not_designed_for": [ "Searching a query spectrum against all deposited raw files." ], "requirements": [ "Search terms or study context; downloads depend on the originating repository." ], "considerations": [ "Coverage depends on participating providers and feed updates. Check study metadata and availability at the original repository." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed. No standalone publication was confirmed in this pass; use the official project citation guidance." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-07", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metabolights", "relationship": "complementary" }, { "slug": "metabolomics-workbench", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metabolomics-workbench", "name": "Metabolomics Workbench", "summary": "Deposit, discover and retrieve metabolomics studies through the National Metabolomics Data Repository.", "description": "Metabolomics Workbench hosts study data and metadata alongside analysis and reference resources. Its repository supports different organisms and measurement platforms; REST access and the mwTab representation support programmatic reuse of study information.", "links": { "primary": "https://www.metabolomicsworkbench.org/", "homepage": "https://www.metabolomicsworkbench.org/", "documentation": "https://www.metabolomicsworkbench.org/tools/mw_rest.php" }, "resource_types": [ "data_repository", "web_service" ], "functions": { "primary": "data_management_sharing", "capabilities": [ "repository_scale_search", "metadata_contextualization", "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "web_app", "api" ], "access": { "model": "free_web_service", "notes": "Public study access is available through the website and REST service. Deposition requires registration and compliance with repository submission guidance." }, "data": { "input_types": [ "raw_instrument_data", "feature_table", "sample_metadata" ], "output_types": [ "raw_instrument_data", "feature_table", "sample_metadata" ], "other_input_formats": [ "Repository submission templates and experimental files" ], "output_formats": [ "json" ], "other_output_formats": [ "mwTab study representation", "Submitted experimental files" ] }, "common_uses": [ "Deposit metabolomics data supporting a publication.", "Retrieve study metadata and measurements for reproducible reanalysis." ], "scope": { "designed_for": [ "Study-level sharing and discovery of MS-based metabolomics data." ], "not_designed_for": [ "Guaranteeing that all deposited studies are directly comparable." ], "requirements": [ "Provide experimental factors, sample identifiers, acquisition and processing details when submitting. For reuse, check the files available for each analysis." ], "considerations": [ "A project may contain several studies or analyses. Retain study and analysis identifiers and cite the project data DOI where provided." ] }, "publications": [ { "doi": "10.1093/nar/gkv1042", "citation": "Sud M, Fahy E, Cotter D, et al. Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, strategies, tutorials and training, and analysis tools. Nucleic Acids Res 44, D463-70 (2016).", "type": "primary" } ], "related_tools": [ { "slug": "metabolights", "relationship": "alternative" }, { "slug": "massive", "relationship": "alternative" } ], "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review on 7 September 2026 against official documentation and the sources listed here. Scope and practical guidance are editorial synthesis; software execution and service availability were not validated." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "aliases": [ "NMDR", "National Metabolomics Data Repository" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metabonaut", "name": "Metabonaut", "summary": "An open educational and reproducible workflow resource for script-based untargeted LC-MS/MS metabolomics using R and Bioconductor packages across preprocessing, QC, statistics, and annotation.", "links": { "primary": "https://rformassspectrometry.github.io/Metabonaut/", "homepage": "https://rformassspectrometry.github.io/Metabonaut/", "repository": "https://github.com/rformassspectrometry/Metabonaut", "documentation": "https://rformassspectrometry.github.io/Metabonaut/" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "preprocessing", "quality_control", "statistics", "annotation_identification" ], "capabilities": [ "provenance_tracking", "chromatogram_building", "peak_detection", "retention_time_alignment", "gap_filling", "library_matching" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "r_package", "notebook" ], "access": { "model": "source_available", "license": "CC-BY-SA-4.0", "notes": "The resource is distributed under CC-BY-SA-4.0. Container and workshop routes provide environments; dependencies retain their own licenses and external services may have separate terms." }, "technical": { "operating_systems": [ "linux", "macos", "windows", "containers" ], "programming_languages": [ "R" ], "containers": [ "Docker" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra", "sample_metadata" ], "output_types": [ "feature_table", "statistical_results", "compound_list", "report" ], "input_formats": [ "mzml", "csv_tsv" ], "other_output_formats": [ "R analysis objects, feature tables and rendered workflow documents" ] }, "publications": [ { "doi": "10.1007/s11306-026-02538-x", "type": "primary", "citation": "Metabonaut: an open educational resource for learning reproducible, script-based untargeted LC-MS/MS metabolomics. Metabolomics (2026)." } ], "common_uses": [ "Learn a reproducible end-to-end untargeted LC-MS/MS workflow in R.", "Reuse worked examples for preprocessing, quality control, statistics, and annotation." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-05", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Learning and reproducing script-based untargeted LC-MS/MS analysis with R/Bioconductor." ], "considerations": [ "Workshop sessions are ephemeral; export work before stopping them. Adapting workflows requires checking acquisition-specific parameters and reference resources." ], "not_designed_for": [ "A single replacement processing engine or automatic transfer of tutorial parameters to every study." ], "requirements": [ "Basic R and Quarto/RMarkdown familiarity; use the documented local R environment, Docker image or Bioconductor workshop session with its example data and dependencies." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "unspecified" ], "ion_mobility": "unspecified" }, "description": "Metabonaut is an executable educational workflow resource for script-based untargeted LC-MS/MS analysis in R and Bioconductor. Its vignettes demonstrate preprocessing, annotation and statistics using a small example dataset and show how to adapt and connect existing packages." }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboprep", "name": "metaboprep", "summary": "An R package for importing processed metabolomics data, applying standardized sample and feature quality-control filters, and generating summary tables and HTML reports.", "links": { "primary": "https://mrcieu.github.io/metaboprep/", "homepage": "https://mrcieu.github.io/metaboprep/", "repository": "https://github.com/MRCIEU/metaboprep", "documentation": "https://mrcieu.github.io/metaboprep/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "secondary": [ "visualization_reporting", "data_management_sharing" ], "capabilities": [ "feature_selection" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "Package DESCRIPTION declares MIT plus the accompanying LICENSE file. Document the installed version because current development interfaces can differ from the original paper." }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "statistical_results", "report" ], "input_formats": [ "csv_tsv", "other" ], "output_formats": [ "csv_tsv", "other" ], "other_input_formats": [ "Excel" ], "other_output_formats": [ "HTML report" ] }, "publications": [ { "doi": "10.1093/bioinformatics/btac059", "type": "methods", "citation": "metaboprep: an R package for preanalysis data description and processing. Bioinformatics (2022)." } ], "common_uses": [ "Apply missingness, total-abundance, and PCA-based QC rules to processed metabolomics datasets.", "Produce standardized QC summaries and reports before statistical analysis." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-04-17", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Quality control and filtering of already quantified metabolomics tables." ], "not_designed_for": [ "Raw chromatographic peak detection or automatic correction of every analytical batch effect." ], "requirements": [ "R and package dependencies, quantified abundance data and aligned sample/feature metadata; select importer and QC thresholds appropriate to the input structure." ], "considerations": [ "Missingness and outlier thresholds can remove biological signals; review exclusion reasons and compare pre/post-QC data before statistical modelling." ] }, "description": "metaboprep prepares quantified metabolomics tables for downstream analysis by summarizing data, filtering samples/features and reporting QC decisions. Its R interface supports abundance matrices and vendor/service tabular exports with sample and feature annotations." }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaboshiny", "name": "MetaboShiny", "summary": "Explore mass-spectrometry metabolomics tables through an R/Shiny interface.", "description": "MetaboShiny combines filtering, normalization, statistical exploration and database-assisted annotation, with local project and database management.", "links": { "primary": "https://github.com/joannawolthuis/MetaboShiny", "documentation": "https://github.com/joannawolthuis/MetaboShiny", "repository": "https://github.com/joannawolthuis/MetaboShiny" }, "resource_types": [ "software_application" ], "functions": { "primary": "statistics", "capabilities": [ "univariate_statistics", "multivariate_statistics" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package", "web_app" ], "access": { "model": "source_available", "notes": "Public research software and container instructions; database licensing is separate." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Feature peak lists and sample metadata" ], "other_output_formats": [ "Saved analysis sessions, tables and plots" ] }, "publications": [ { "citation": "Wolthuis JC, Magnusdottir S, Pras-Raves M, et al. MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data. Metabolomics 16, 99 (2020).", "type": "primary", "doi": "10.1007/s11306-020-01717-8" } ], "common_uses": [ "Explore feature abundances and sample groups interactively.", "Generate mass-compatible compound candidates from selected databases." ], "scope": { "designed_for": [ "Explore feature abundances and sample groups interactively." ], "not_designed_for": [ "Treating m/z database matches as definitive compound identities." ], "requirements": [ "Feature tables, sample metadata and a configured R or container installation." ], "considerations": [ "Database snapshots require management; preserve normalization choices and check installation instructions against current dependencies." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metaboanalyst", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metabosignal", "name": "MetaboSignal", "summary": "Explore relationships between genes and metabolites in combined metabolic and signaling networks.", "description": "MetaboSignal merges and analyzes KEGG pathway information to study network topology and possible connections to metabolic phenotypes.", "links": { "primary": "https://bioconductor.org/packages/MetaboSignal/", "documentation": "https://bioconductor.org/packages/MetaboSignal/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "multiomics_integration", "capabilities": [ "network_visualization" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Bioconductor software; pathway-data licensing is separate." }, "data": { "input_types": [ "compound_list", "pathway_data" ], "output_types": [ "pathway_data", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "KEGG pathways and gene/metabolite identifiers" ], "other_output_formats": [ "R graphs and network summaries" ] }, "publications": [ { "citation": "Rodriguez-Martinez A, Ayala R, Posma JM, et al. MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways. Bioinformatics 33, 773-775 (2017).", "type": "primary", "doi": "10.1093/bioinformatics/btw697" } ], "common_uses": [ "Overlay metabolite and gene candidates on a shared network.", "Investigate paths linking signaling genes to metabolites." ], "scope": { "designed_for": [ "Overlay metabolite and gene candidates on a shared network." ], "not_designed_for": [ "Interpreting network distance as demonstrated causal regulation." ], "requirements": [ "R, compatible pathway access and recognized gene/metabolite identifiers." ], "considerations": [ "Topology depends on the underlying knowledgebase and identifier mapping; KEGG access and reuse terms apply separately." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "reactome", "relationship": "complementary" }, { "slug": "omicspls", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metapro", "name": "MetaPro", "summary": "MetaPro is a locally deployed web application for targeted LC-MS analysis, batch inspection and spectral-library curation.", "links": { "primary": "https://github.com/CSi-Studio/MetaPro", "repository": "https://github.com/CSi-Studio/MetaPro", "documentation": "https://csi-studio.github.io/MetaPro/guide/user-manual.html" }, "resource_types": [ "software_application" ], "functions": { "primary": "quality_control", "secondary": [ "annotation_identification", "visualization_reporting" ], "capabilities": [ "library_matching", "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "web_app" ], "access": { "model": "free_academic", "notes": "README restricts this version to academic use and asks commercial users to contact the developers. Local deployment and supporting database/software requirements apply." }, "technical": { "operating_systems": [ "windows", "linux", "macos" ] }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "feature_table", "compound_list", "report" ], "other_input_formats": [ "DDA Aird files and formatted compound/spectral libraries" ], "other_output_formats": [ "Curated spectral libraries and targeted-analysis result exports" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2023-04-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MetaPro is a locally deployed web application for targeted LC-MS analysis, batch inspection and spectral-library curation. Users inspect compound detections across runs and export curated libraries and analysis results.", "scope": { "designed_for": [ "MetaPro is a locally deployed web application for targeted LC-MS analysis, batch inspection and spectral-library curation." ], "not_designed_for": [ "General DIA analysis or automatic validation of targeted detections without reviewing the batch evidence." ], "requirements": [ "A supported local/server installation with MongoDB/Redis dependencies, AirdPro-converted DDA data and a compound library in the required format; manual lists at least 16 GB RAM." ], "considerations": [ "The manual restricts the documented workflow to DDA. Check compound-library content, integration and spectrum quality before exporting curated results." ] }, "common_uses": [ "MetaPro is a locally deployed web application for targeted LC-MS analysis, batch inspection and spectral-library curation." ], "publications": [ { "doi": "10.1007/s11306-023-02018-6", "type": "primary", "citation": "MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation. Metabolomics (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metaspace", "name": "METASPACE", "summary": "Annotate imaging mass-spectrometry datasets and explore community-contributed spatial metabolomes.", "description": "METASPACE combines a cloud annotation engine with a public knowledgebase and interactive ion-image exploration. Its Python client supports programmatic analysis of annotations.", "links": { "primary": "https://metaspace2020.org/", "documentation": "https://github.com/metaspace2020/metaspace", "repository": "https://github.com/metaspace2020/metaspace" }, "resource_types": [ "workflow_platform" ], "functions": { "primary": "reference_data_search", "capabilities": [ "metadata_contextualization", "interactive_dashboard" ] }, "platforms": [ "imaging_ms" ], "interfaces": [ "web_app", "python_package", "api" ], "access": { "model": "free_web_service", "notes": "Public platform with Apache-2.0 source code; dataset access and reuse depend on the individual deposit." }, "data": { "input_types": [ "imaging_data", "sample_metadata" ], "output_types": [ "imaging_data", "compound_list" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Imaging-MS files and acquisition metadata" ], "other_output_formats": [ "Ion images and annotation records" ] }, "publications": [ { "citation": "Alexandrov T, Ovchinnikova K, Palmer A, et al. METASPACE: A community-populated knowledge base of spatial metabolomes in health and disease. bioRxiv (2019). Preprint; not peer reviewed.", "type": "primary", "doi": "10.1101/539478" } ], "common_uses": [ "Find public spatial metabolomics datasets.", "Inspect molecular annotations alongside tissue ion images." ], "scope": { "designed_for": [ "Find public spatial metabolomics datasets." ], "not_designed_for": [ "Treating a spatial annotation as complete isomer or stereochemical confirmation." ], "requirements": [ "For new analyses, compatible imaging-MS data and acquisition metadata; browsing public results is a separate workflow." ], "considerations": [ "Review annotation confidence, adduct assumptions and database choice; spatial colocalization does not by itself imply a biological interaction." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metabolomics-workbench", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metdna3", "name": "MetDNA3", "summary": "A metabolite-annotation platform using knowledge-driven and data-driven two-layer networking with recursive annotation propagation for untargeted metabolomics.", "links": { "primary": "http://metdna.zhulab.cn/", "web_app": "http://metdna.zhulab.cn/", "repository": "https://github.com/ZhuMetLab/MrnAnnoAlgo3", "documentation": "https://github.com/ZhuMetLab/MrnAnnoAlgo3#readme" }, "resource_types": [ "web_service", "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "molecular_networking" ], "capabilities": [ "structure_database_search", "identification_confidence" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "r_package", "web_app" ], "access": { "model": "source_available", "notes": "The core repository states CC-BY-NC-ND-4.0. Full web-service functionality and account access are distinct from the public core package; no open-source or unrestricted-reuse claim is made.", "license": "CC-BY-NC-ND-4.0" }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "compound_list", "statistical_results" ], "other_input_formats": [ "MRN3 MS1 tables, matched MS2 lists and seed-annotation tables" ], "other_output_formats": [ "Network-propagated candidate annotations and confidence/redundancy results" ] }, "common_uses": [ "Propagate metabolite annotations across related LC-MS features using complementary network layers." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-06-27", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MetDNA3 recursively annotates metabolites using interacting knowledge-driven and experimental-similarity network layers. Its public MrnAnnoAlgo3 repository contains the core annotation algorithm; the registered web service supplies the complete platform workflow.", "scope": { "designed_for": [ "A metabolite-annotation platform using knowledge-driven and data-driven two-layer networking with recursive annotation propagation for untargeted metabolomics." ], "not_designed_for": [ "A claim that installing the core R repository reproduces every web-service module or experimentally confirms propagated candidates." ], "requirements": [ "For the core algorithm, prepared MS1 feature data, matching MS2 lists and seed tables in MRN3 format with suitable instrument/LC/polarity settings. The complete hosted route requires registration and its documented uploads." ], "considerations": [ "Seed reliability, network coverage and acquisition compatibility influence propagation. The core function expects structured data rather than arbitrary raw files; evaluate confidence and alternative annotations." ] }, "publications": [ { "doi": "10.1038/s41467-025-63536-6", "type": "primary", "citation": "Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics. Nature Communications (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "meteor", "name": "MeTEor", "summary": "MeTEor explores longitudinal metabolomics tables with repeated-measures statistical models, dimensionality reduction, prediction models and interactive plots.", "links": { "primary": "https://github.com/scibiome/meteor", "repository": "https://github.com/scibiome/meteor", "documentation": "https://github.com/scibiome/meteor#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "statistics", "secondary": [ "visualization_reporting", "machine_learning" ], "capabilities": [ "univariate_statistics", "multivariate_statistics", "interactive_dashboard" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling" ], "interfaces": [ "r_package", "web_app" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform", "browser" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Processed longitudinal metabolomics tables with subject/time metadata" ], "other_output_formats": [ "Statistical/model results, network views and exploratory plots" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-10-06", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MeTEor explores longitudinal metabolomics tables with repeated-measures statistical models, dimensionality reduction, prediction models and interactive plots. Its R/Shiny interface also supports identifier/pathway mapping and network views.", "scope": { "designed_for": [ "MeTEor explores longitudinal metabolomics tables with repeated-measures statistical models, dimensionality reduction, prediction models and interactive plots." ], "not_designed_for": [ "Raw peak extraction or reliable longitudinal inference without accounting for study design, repeated observations and missingness." ], "requirements": [ "Docker or the documented R plus Miniconda environment and a processed abundance dataset with longitudinal subject, time and group metadata." ], "considerations": [ "Choose models consistent with the sampling structure and prevent leakage in prediction. Identifier and pathway mapping can depend on external services." ] }, "common_uses": [ "MeTEor explores longitudinal metabolomics tables with repeated-measures statistical models, dimensionality reduction, prediction models and interactive plots." ], "publications": [ { "doi": "10.1093/bioadv/vbae178", "type": "primary", "citation": "MeTEor: an R Shiny app for exploring longitudinal metabolomics data. Bioinformatics Advances (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metexplore", "name": "MetExplore", "aliases": [], "summary": "MetExplore is an open-access web server that provides interactive tools for metabolic network analysis and visualization. The platform offers:\nA collection of curated genome-scale metabolic networks for multiple organisms, together with the possibility to import custom metabolic networks from SBML files.\nA metabolite identification module that maps experimental metabolites onto metabolic networks using ChEBI identifiers.\nPathway overrepresentation analysis to identify significantly enriched m...", "links": { "primary": "https://metexplore.toulouse.inrae.fr/metexplore3", "repository": "https://forge.inrae.fr/metexplore/metexplore3", "web_app": "https://metexplore.toulouse.inrae.fr/metexplore3/", "homepage": "https://metexplore.toulouse.inrae.fr/metexplore3" }, "resource_types": [ "web_service" ], "functions": { "primary": "pathway_interpretation", "secondary": [ "multiomics_integration", "visualization_reporting" ], "capabilities": [] }, "platforms": [ "platform_independent" ], "analysis_types": [], "interfaces": [ "web_app" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1093/nar/gky301", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-15" }, "credits": [ { "name": "Ludovic Cottret", "role": "developer", "orcid": "0000-0001-7418-7750" } ], "provenance": { "submitted_by": "developer", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/34", "developer_verified": false, "submitter_name": "Ludovic Cottret", "public_attribution_consent": true, "submitter_orcid": "0000-0001-7418-7750" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-15", "updated_at": "2026-09-15", "last_verified": "2026-09-15" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metfrag", "name": "MetFrag", "summary": "Rank candidate molecular structures by comparing predicted fragments with an experimental MS/MS spectrum.", "description": "MetFrag retrieves structures using precursor information and scores their in silico fragments against measured peaks. Optional evidence scores can supplement fragmentation. Use it after extracting spectra when you need a transparent, configurable candidate-ranking workflow.", "links": { "primary": "https://ipb-halle.github.io/MetFrag/", "homepage": "https://ipb-halle.github.io/MetFrag/", "documentation": "https://ipb-halle.github.io/MetFrag/projects/metfragcl/" }, "resource_types": [ "software_application", "web_service" ], "functions": { "primary": "annotation_identification", "capabilities": [ "structure_database_search", "in_silico_spectra" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "interfaces": [ "web_app", "command_line", "r_package" ], "access": { "model": "open_source", "license": "LGPL-2.1-or-later", "notes": "Available through the web interface, Java command line and R interface. External databases may have separate access conditions." }, "data": { "input_types": [ "spectra", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "other_input_formats": [ "Text peak list (m/z and intensity)", "MetFrag parameter file" ], "output_formats": [ "sdf", "csv_tsv" ] }, "common_uses": [ "Prioritize structures for an unknown MS/MS spectrum.", "Repeat annotation with an explicitly recorded candidate database and scoring configuration." ], "scope": { "designed_for": [ "Database-constrained structure annotation from prepared spectra." ], "not_designed_for": [ "Detecting or aligning chromatographic features from raw files." ], "requirements": [ "Supply precursor mass or formula, ion/adduct settings, a peak list and mass tolerances; Java is needed for local command-line use." ], "considerations": [ "A correct structure must be represented in the candidate set. A high rank is supporting evidence, not confirmation with an authentic standard." ] }, "publications": [ { "doi": "10.1186/s13321-016-0115-9", "citation": "Ruttkies C, Schymanski EL, Wolf S, et al. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminform 8, 3 (2016).", "type": "primary" } ], "related_tools": [ { "slug": "csi-fingerid", "relationship": "alternative" }, { "slug": "mona", "relationship": "complementary" } ], "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review on 7 September 2026 against official documentation and the sources listed here. Scope and practical guidance are editorial synthesis; software execution and service availability were not validated." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metgem", "name": "MetGem", "summary": "Local desktop generation and exploration of MS/MS similarity networks and two-dimensional spectral projections.", "links": { "primary": "https://metgem.github.io/", "documentation": "https://metgem.readthedocs.io/en/latest/user_manual.html", "repository": "https://github.com/metgem/metgem" }, "resource_types": [ "software_application" ], "functions": { "primary": "molecular_networking", "secondary": [ "visualization_reporting", "spectral_analysis" ], "capabilities": [ "network_visualization" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "Open-source desktop software with prebuilt and source installation routes. Check operating-system support in the current release instructions; documentation and application versions may differ." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official project documentation and direct tool publications reviewed on 2026-09-12. Included for downstream MS metabolomics. No installation, dataset analysis or independent performance benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "data": { "input_types": [ "spectra" ], "output_types": [ "report" ], "other_output_formats": [ "Interactive network views and exported analysis results" ], "input_formats": [ "mgf", "msp" ], "other_input_formats": [ "Optional CSV spectrum metadata" ] }, "publications": [ { "citation": "Olivon F, Elie N, Grelier G, et al. MetGem Software for the Generation of Molecular Networks Based on the t-SNE Algorithm. Analytical Chemistry 90, 13900-13908 (2018).", "type": "primary", "doi": "10.1021/acs.analchem.8b03099", "pmid": "30335965" } ], "related_tools": [ { "slug": "gnps", "relationship": "alternative", "note": "A local alternative for the spectral-networking part of a workflow, not a replacement for all GNPS services." }, { "slug": "cytoscape", "relationship": "complementary", "note": "General network visualization; check export formats before transferring results." } ], "description": "MetGem loads tandem mass spectra and optional metadata, computes spectral similarities and offers molecular-network or projection views. It provides a local route for exploring processed LC-MS/MS spectra. Graph layouts and t-SNE projections are different representations and should not be interpreted as validated chemical structures.", "common_uses": [ "Build local molecular networks from processed MS/MS spectra.", "Compare network views with spectral projections." ], "scope": { "designed_for": [ "Spectral-similarity calculation and interactive exploration." ], "not_designed_for": [ "Vendor raw-file preprocessing or confident identification from projection position." ], "requirements": [ "Processed MGF or MSP tandem spectra and suitable precursor metadata." ], "considerations": [ "Record spectral filters, tolerances and similarity thresholds.", "t-SNE emphasizes local relationships; global distances and layout changes can mislead." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metid", "name": "metID", "summary": "Annotate LC-MS features using accurate mass, retention time and tandem mass spectra in R.", "description": "metID supports matching against in-house and public reference databases and can be used independently or within the TidyMass ecosystem.", "links": { "primary": "https://metid.tidymass.org/", "documentation": "https://metid.tidymass.org/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "library_matching", "structure_database_search" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "GPL-2 package; reference databases retain their own licenses." }, "data": { "input_types": [ "feature_table", "spectra", "spectral_library" ], "output_types": [ "compound_list" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "R feature, spectral and reference database objects" ], "other_output_formats": [ "R compound-annotation objects" ] }, "publications": [ { "citation": "Shen X, Wu S, Liang L, et al. metID: an R package for automatable compound annotation for LC-MS-based data. Bioinformatics 38, 568-569 (2022).", "type": "primary", "doi": "10.1093/bioinformatics/btab583" } ], "common_uses": [ "Automate matching against an in-house reference library.", "Combine mass and MS/MS evidence with retention information when available." ], "scope": { "designed_for": [ "Automate matching against an in-house reference library." ], "not_designed_for": [ "Assigning the same confidence to mass-only and reference-standard matches." ], "requirements": [ "R, feature or spectral data and a compatible reference database." ], "considerations": [ "Retention times are method-dependent; choose tolerances and report the evidence supporting each annotation." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "tidymass", "relationship": "complementary" }, { "slug": "metaboannotation", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "metlinkr", "name": "MetLinkR", "aliases": [], "summary": "MetLinkR is an R package used for cross-linking of metabolite identifiers across disparate data sets. It uses RefMet and RaMP-DB links to recognize metabolites referred to by different names or database identifiers. MetLinkR can be used as an R package or accessed through the RaMP-DB web interface.", "links": { "primary": "https://rampdb.nih.gov/identifier-harmonization", "repository": "https://github.com/ncats/MetLinkR", "homepage": "https://rampdb.nih.gov/identifier-harmonization" }, "resource_types": [ "programming_package" ], "functions": { "primary": "reference_data_search", "secondary": [], "capabilities": [] }, "platforms": [ "platform_independent" ], "analysis_types": [], "interfaces": [ "r_package" ], "access": { "model": "open_source" }, "publications": [ { "citation": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12053952/", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-18" }, "credits": [ { "name": "Andrew Patt", "role": "developer", "orcid": "0000-0002-6373-9545" } ], "provenance": { "submitted_by": "developer", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/45", "developer_verified": false, "submitter_name": "Andrew Patt", "public_attribution_consent": true, "submitter_orcid": "0000-0002-6373-9545" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-18", "updated_at": "2026-09-18", "last_verified": "2026-09-18" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "metscape", "name": "MetScape", "summary": "Cytoscape app for interpreting identified metabolites and expression data in metabolic and correlation networks.", "links": { "primary": "https://apps.cytoscape.org/apps/metscape", "documentation": "https://metscape.med.umich.edu/metscape3/index.html" }, "resource_types": [ "software_application" ], "functions": { "primary": "pathway_interpretation", "secondary": [ "visualization_reporting", "multiomics_integration" ], "capabilities": [ "network_visualization" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "desktop_gui" ], "access": { "model": "unknown", "notes": "Download is offered through the Cytoscape App Store. A current explicit app license was not confirmed in the reviewed pages; do not infer the app license from Cytoscape. The listing reports version 3.1.3 (2017) and compatibility with Cytoscape 3.4; compatibility with newer releases was not tested." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official project documentation and direct tool publications reviewed on 2026-09-12. Included for downstream MS metabolomics. No installation, dataset analysis or independent performance benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "data": { "input_types": [ "compound_list", "pathway_data" ], "output_types": [ "report", "pathway_data" ], "other_output_formats": [ "Interactive network views and exported analysis results" ], "other_input_formats": [ "CSV, TSV or Excel compound/gene tables; supported compound and gene identifiers" ] }, "related_tools": [ { "slug": "cytoscape", "relationship": "integration", "note": "Installed as an app within desktop Cytoscape; compatibility is version dependent." } ], "description": "MetScape maps compound and gene identifiers into metabolic networks and supports exploration of metabolite correlation networks. Its reference context is human metabolism, including mappings from supported mouse and rat inputs. It is included for interpretation of MS-derived results after annotation, not as a method for assigning structures from mass spectra.", "common_uses": [ "Place annotated MS metabolites into metabolic context.", "Explore metabolite and gene relationships in Cytoscape." ], "scope": { "designed_for": [ "Supported identifier mapping and metabolic-network interpretation." ], "not_designed_for": [ "Raw MS preprocessing or metabolite identification from accurate mass alone." ], "requirements": [ "Cytoscape with a compatible MetScape app and supported identifiers." ], "considerations": [ "Reference coverage and identifier mapping limit which metabolites appear.", "Legacy installation and remote data-service availability require verification before planning a study." ] }, "publications": [ { "citation": "Karnovsky A, et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28, 373-380 (2012).", "doi": "10.1093/bioinformatics/btr661", "type": "primary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mibig", "name": "MIBiG", "summary": "Curated biosynthetic gene-cluster and product references for interpreting MS-based natural-product and metabologenomics results.", "links": { "primary": "https://mibig.secondarymetabolites.org/", "documentation": "https://zenodo.org/records/14169073", "download": "https://mibig.secondarymetabolites.org/download" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "metadata_contextualization" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "license": "CC-BY-4.0", "notes": "Public browser access and downloadable reference data. The archived MIBiG 4.0 JSON dataset is CC-BY-4.0; preserve attribution and record the actual release used. This is a data license, not a claim about every linked resource or software component." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation, repository or archived release metadata and direct resource publications reviewed on 2026-09-12. Included for MS-based natural-product research. No installation, biological validation or independent benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "molecular_structures", "report" ], "output_formats": [ "json" ], "other_input_formats": [ "Biosynthetic cluster identifiers or product searches" ], "other_output_formats": [ "Reference records linking genomic regions, products and supporting evidence" ] }, "publications": [ { "citation": "Zdouc MM, et al. MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration. Nucleic Acids Research 53, D678-D690 (2025).", "doi": "10.1093/nar/gkae1115", "type": "primary" } ], "related_tools": [ { "slug": "nplinker", "relationship": "integration", "note": "Supplies biosynthetic reference context; select a MIBiG version supported by the NPLinker configuration." } ], "description": "MIBiG records experimentally characterized biosynthetic gene clusters and their associated products using a shared data standard. Researchers can inspect reference evidence when relating MS annotations to biosynthetic hypotheses. It is a biosynthesis knowledgebase, not an MS/MS spectral library or a standalone annotation engine.", "common_uses": [ "Check reference evidence for known natural-product biosynthesis.", "Supply versioned BGC/product context for metabologenomics analysis." ], "scope": { "designed_for": [ "Reference lookup for experimentally characterized biosynthetic gene clusters." ], "not_designed_for": [ "Direct identification of an unknown MS/MS spectrum or exhaustive coverage of all biosynthetic diversity." ], "requirements": [ "A relevant cluster, organism or product query; compatible dataset version for downstream software." ], "considerations": [ "Inspect record-level experimental evidence and completeness before interpreting a match.", "Similarity to a known cluster does not prove production of the same compound in a new sample." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "microbemasst", "name": "microbeMASST", "summary": "A taxonomically informed GNPS search service that connects query MS/MS spectra to matching spectra from curated bacterial, fungal, and archaeal monoculture datasets.", "description": "microbeMASST is a domain-specific repository search resource that contextualizes known or unknown tandem mass spectra against a curated reference collection of microbial monoculture extracts. Searches use accelerated GNPS/FASST infrastructure and organize matches in taxonomic trees, helping users ask which cultured microorganisms have produced a compound or a spectrally related molecule. Batch-search code is also available for larger analyses.", "links": { "primary": "https://masst.gnps2.org/microbemasst/", "web_app": "https://masst.gnps2.org/microbemasst/", "repository": "https://github.com/robinschmid/microbe_masst", "documentation": "https://github.com/robinschmid/microbe_masst" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "secondary": [ "spectral_analysis", "data_management_sharing" ], "capabilities": [ "repository_scale_search", "metadata_contextualization", "library_matching" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app", "python_package" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.1038/s41564-023-01575-9", "citation": "Zuffa S, Schmid R, Bauermeister A, et al. microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data. Nat Microbiol 9, 336-345 (2024).", "type": "primary" } ], "common_uses": [ "Associate unknown or known MS/MS spectra with potential microbial producers represented in monoculture reference data.", "Explore exact and analog spectral matches across bacterial, fungal, and archaeal taxonomy.", "Batch-query many spectra to annotate microbial context in larger untargeted datasets.", "Use taxonomic matches to prioritize cultured organisms for targeted follow-up experiments." ], "scope": { "designed_for": [ "Microbial-origin hypothesis generation and taxonomic contextualization of MS/MS features." ], "requirements": [ "A query MS/MS spectrum provided as a USI, library identifier, peak list, or compatible batch input." ], "considerations": [ "A spectral occurrence in a cultured microorganism supports producer/context hypotheses but does not establish biosynthetic origin in every sample.", "Reference coverage is uneven across taxa and reflects community deposition and metadata curation.", "Inspect medium and blank controls in the source study; a cultured-organism association can include compounds from growth media." ], "not_designed_for": [ "Assigning the producer of a molecule in a mixed community solely from a monoculture spectral match." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "fasst", "relationship": "uses" }, { "slug": "redu", "relationship": "uses" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser", "cross_platform" ], "programming_languages": [ "Python" ], "api_available": false }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra", "sample_metadata", "report" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "Universal Spectrum Identifier (USI) or peak list" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "microbiomemasst", "name": "microbiomeMASST", "summary": "A GNPS-associated metadata-network search resource for mapping query metabolites and MS/MS spectra across microbiome metabolomics studies, sample contexts, hosts, and microbial metadata.", "description": "microbiomeMASST extends domain-specific repository searching from curated monocultures to a harmonized network of microbiome metabolomics datasets and metadata. It is designed to reveal where molecules occur across host-associated and microbial study contexts and to make large public microbiome metabolomics collections searchable through spectrum-to-metadata relationships. The scientific description is currently available as a 2026 preprint.", "links": { "primary": "https://apps.gnps2.org/microbiomemasst", "web_app": "https://apps.gnps2.org/microbiomemasst", "repository": "https://github.com/robinschmid/microbe_masst", "documentation": "https://github.com/robinschmid/microbe_masst" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "secondary": [ "data_management_sharing", "spectral_analysis" ], "capabilities": [ "repository_scale_search", "metadata_contextualization", "network_visualization" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling", "multiomics" ], "interfaces": [ "web_app", "python_package" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.64898/2026.02.04.703849", "citation": "Charron-Lamoureux V, Xing S, Patan A, et al. A searchable metadata network graph for microbiome metabolomics. bioRxiv (2026). Preprint; not peer reviewed.", "type": "primary" } ], "common_uses": [ "Map query spectra across harmonized microbiome metabolomics datasets and metadata contexts.", "Explore host, sample, microbial, and experimental contexts associated with matching spectra.", "Generate hypotheses about metabolite distributions across microbiome studies using public data.", "Follow spectrum-to-metadata associations to source studies for focused reanalysis." ], "scope": { "designed_for": [ "Repository-scale contextualization of metabolites across microbiome metabolomics metadata networks." ], "requirements": [ "Query MS/MS information and public-data metadata indexed by the current resource." ], "considerations": [ "The primary scientific description is a preprint and the underlying metadata network is actively evolving.", "Associations from public-data co-occurrence are hypothesis-generating and can reflect sampling, deposition, or study-design biases.", "Check study membership and available controls because repeated samples and heterogeneous studies are not independent biological replications." ], "not_designed_for": [ "Inferring causation or identifying a microbial producer from cross-study metadata associations alone." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "fasst", "relationship": "uses" }, { "slug": "redu", "relationship": "uses" }, { "slug": "microbemasst", "relationship": "complementary" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser", "cross_platform" ], "programming_languages": [ "Python" ], "api_available": false }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra", "sample_metadata", "report" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "Universal Spectrum Identifier (USI) or peak list" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mist-cf", "name": "MIST-CF", "summary": "A machine-learning tool for ranking molecular formula and adduct assignments directly from MS/MS spectra using a transformer-based model without requiring spectral-library matching.", "links": { "primary": "https://github.com/samgoldman97/mist-cf", "repository": "https://github.com/samgoldman97/mist-cf", "documentation": "https://github.com/samgoldman97/mist-cf#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "molecular_formula_prediction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT-licensed implementation. A public NPLIB1-trained model is linked; NIST20-trained model access is separately restricted to users with a NIST license and is not equivalent to the public checkpoint." }, "technical": { "operating_systems": [ "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1021/acs.jcim.3c01082", "type": "primary", "citation": "MIST-CF: Chemical Formula Inference from Tandem Mass Spectra. Journal of Chemical Information and Modeling 64, 2421–2431 (2024; first online 2023)." } ], "common_uses": [ "Rank chemical-formula and adduct candidates for unknown MS/MS spectra.", "Apply pretrained models to public natural-product or CASMI-style spectra." ], "maintenance": { "status": "unclear", "repository_last_activity": "2023-09-20", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "De novo formula and adduct ranking from small-molecule MS/MS spectra." ], "requirements": [ "Python environment, pretrained weights and positive-ion MS/MS input. The supplied workflow requires SIRIUS decomposition; an interactive example also supports user-specified candidates." ], "considerations": [ "Formula/adduct rankings remain hypotheses and depend on instrument and training-domain coverage. Public and NIST-trained models should not be treated as interchangeable." ], "not_designed_for": [ "Full molecular-structure identification or an established negative-ion workflow." ] }, "description": "MIST-CF ranks molecular-formula and adduct candidates using precursor mass and tandem spectral evidence with a formula-transformer model. The documented workflow enumerates formulas using SIRIUS decomposition and then scores them without constructing fragmentation trees." }, { "$schema": "../../schemas/tool.schema.json", "slug": "mist", "name": "MIST", "summary": "Predict molecular fingerprints from tandem mass spectra and retrieve candidate structures.", "description": "Metabolite Inference with Spectrum Transformers encodes chemical formula information associated with fragments. Fingerprint and contrastive models support database-based structure ranking.", "links": { "primary": "https://github.com/samgoldman97/mist", "documentation": "https://github.com/samgoldman97/mist", "repository": "https://github.com/samgoldman97/mist" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "structure_database_search", "substructure_prediction" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "source_available", "notes": "Research code and pretrained models are public; check code and dataset licenses separately." }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list", "statistical_results" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Peak lists and metadata in the quickstart layout" ], "other_output_formats": [ "Fingerprint predictions and retrieval results" ] }, "publications": [ { "citation": "Goldman, Wohlwend, Stražar, et al. Annotating metabolite mass spectra with domain-inspired chemical formula transformers. Nature Machine Intelligence 5, 965-979 (2023).", "type": "primary", "doi": "10.1038/s42256-023-00708-3" } ], "common_uses": [ "Generate fingerprint predictions for query spectra.", "Benchmark candidate retrieval with supplied research workflows." ], "scope": { "designed_for": [ "Generate fingerprint predictions for query spectra." ], "not_designed_for": [ "Finding an absent database structure through retrieval alone." ], "requirements": [ "Python, compatible model weights, spectral preprocessing and candidate data in the documented layout." ], "considerations": [ "The repository notes changes from the published implementation and proprietary data in some benchmarks; pin the code and evaluation set." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "csi-fingerid", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mixomics", "name": "mixOmics", "aliases": [ "DIABLO", "MINT" ], "summary": "mixOmics is an R package with tools for integrating multi-omic datasets, such as proteomics, metabolomics, metagenomics, microbiome, lipidomics, and more. It can assist in data visualization and advanced multivariate techniques for integration of varied omics studies.", "links": { "primary": "https://mixomics.org/", "repository": "https://github.com/mixOmics-org/mixOmics", "documentation": "https://www.rdocumentation.org/packages/mixOmics/versions/6.3.2", "homepage": "https://mixomics.org/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "multiomics_integration", "secondary": [ "statistics", "visualization_reporting", "pathway_interpretation" ], "capabilities": [] }, "platforms": [ "platform_independent" ], "analysis_types": [], "interfaces": [ "r_package" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1371/journal.pcbi.1005752", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-16" }, "credits": [], "provenance": { "submitted_by": "user", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/40", "developer_verified": false, "submitter_name": "Daniel J. Otto", "public_attribution_consent": true, "submitter_orcid": "0009-0001-4165-3484" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-16", "updated_at": "2026-09-16", "last_verified": "2026-09-16" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mobilipid", "name": "MobiLipid", "summary": "Assess and correct lipid collision-cross-section bias using labeled internal standards.", "description": "MobiLipid provides R Markdown workflows and a labeled-lipid reference library for CCS quality control in ion-mobility MS lipidomics. It estimates bias and class/adduct-specific correction functions. Correction functions are fitted by linear regression.", "links": { "primary": "https://github.com/FelinaHildebrand/MobiLipid", "repository": "https://github.com/FelinaHildebrand/MobiLipid", "documentation": "https://github.com/FelinaHildebrand/MobiLipid#readme" }, "platforms": [ "lc_ms" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "notebook" ], "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "quality_control", "capabilities": [ "univariate_statistics" ] }, "access": { "model": "source_available", "notes": "Code is publicly available; an explicit software license was not identified in the reviewed repository. The article license does not establish code reuse terms." }, "data": { "input_types": [ "feature_table" ], "other_input_formats": [ "CSV: File, LipidClass, LipidSpecies, Adduct, Label, CCS", "Provided U13C lipid CCS reference library" ], "output_types": [ "feature_table", "report" ], "other_output_formats": [ "Corrected CCS tables (CSV) and HTML/PDF reports" ] }, "common_uses": [ "Check CCS bias in lipidomics measurements containing labeled internal standards." ], "scope": { "designed_for": [ "Internal-standard-based CCS assessment and correction." ], "not_designed_for": [ "Raw spectrum processing, lipid identification or metabolic flux estimation." ], "requirements": [ "Ion-mobility measurements of samples spiked with uniformly 13C-labeled lipid standards.", "R/RStudio and documented dependencies; correction needs at least three standards per supported class/adduct combination." ], "considerations": [ "Vendor-required instrument calibration remains necessary.", "Correction scope depends on represented lipid classes and adducts; do not extrapolate validation to all lipids." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c01253", "type": "primary", "citation": "MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility–Mass Spectrometry Lipidomics by Internal Standardization. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Selected from the Enveda intake. Official documentation and primary-paper DOI/title/year checked on 2026-10-21. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mobilitytransformr", "name": "MobilityTransformR", "summary": "MobilityTransformR converts CE-MS migration times to an effective-mobility scale using added mobility markers.", "links": { "primary": "https://github.com/LiesaSalzer/MobilityTransformR", "repository": "https://github.com/LiesaSalzer/MobilityTransformR", "documentation": "https://github.com/LiesaSalzer/MobilityTransformR/blob/main/vignettes/MobilityTransformR.Rmd" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_conversion", "secondary": [], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "ce_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "Artistic-2.0", "notes": "Artistic-2.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "spectra", "feature_table" ], "output_types": [ "spectra", "feature_table" ], "other_input_formats": [ "CE-MS numeric migration times or Spectra/OnDiskMSnExp objects" ], "other_output_formats": [ "Mobility-transformed R objects and mzML exports" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2022-03-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MobilityTransformR converts CE-MS migration times to an effective-mobility scale using added mobility markers. Its R/Bioconductor workflow transforms numeric vectors or supported spectral objects and can export transformed runs for downstream analysis.", "scope": { "designed_for": [ "MobilityTransformR converts CE-MS migration times to an effective-mobility scale using added mobility markers." ], "not_designed_for": [ "Ion-mobility CCS prediction or correction without suitable electrophoretic marker and acquisition information." ], "requirements": [ "R 4.2 or newer with the documented Bioconductor packages, marker migration times and known mobility information; voltage/capillary parameters are needed for relevant single-marker modes." ], "considerations": [ "Identify marker peaks carefully and retain units. The exported time axis represents effective electrophoretic mobility rather than ordinary chromatographic retention time." ] }, "common_uses": [ "MobilityTransformR converts CE-MS migration times to an effective-mobility scale using added mobility markers." ], "publications": [ { "doi": "10.1093/bioinformatics/btac441", "type": "primary", "citation": "MobilityTransformR: an R package for effective mobility transformation of CE-MS data. Bioinformatics (2022)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "moccal", "name": "MOCCal", "summary": "MOCCal calibrates traveling-wave ion-mobility measurements and calculates class-specific experimental CCS values.", "links": { "primary": "https://github.com/HinesLab/MOCCal", "repository": "https://github.com/HinesLab/MOCCal", "documentation": "https://github.com/HinesLab/MOCCal#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "secondary": [ "annotation_identification" ], "capabilities": [ "multivariate_statistics" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Template-formatted calibration/experimental data or raw-route inputs" ], "other_output_formats": [ "Calibration fits, biomolecular-class assignments and calculated CCS" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-01-24", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MOCCal calibrates traveling-wave ion-mobility measurements and calculates class-specific experimental CCS values. It includes processed-drift-time and raw-calibration-data routes and can assign biomolecular classes for class-specific calculations.", "scope": { "designed_for": [ "MOCCal calibrates traveling-wave ion-mobility measurements and calculates class-specific experimental CCS values." ], "not_designed_for": [ "Structure-to-CCS prediction or proof of molecular identity from biomolecular-class assignment." ], "requirements": [ "Calibration/experimental data in the supplied templates; the raw-data route additionally requires the documented DEIMoS environment, while the processed-data executable needs its Output directory." ], "considerations": [ "TWIM arrival time and drift time differ physically even where the interface uses the terms interchangeably. Calibrant choice, charge/adduct and class assumptions affect CCS results." ] }, "common_uses": [ "MOCCal calibrates traveling-wave ion-mobility measurements and calculates class-specific experimental CCS values." ], "publications": [ { "doi": "10.1021/acs.analchem.3c04290", "type": "primary", "citation": "MOCCal: A Multiomic CCS Calibrator for Traveling Wave Ion Mobility Mass Spectrometry. Analytical Chemistry (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "modifinder", "name": "ModiFinder", "summary": "A GNPS2-associated computational tool for localizing structural modification sites by aligning MS/MS spectra of a known compound and a structurally related unknown analogue.", "description": "ModiFinder compares tandem mass spectra from structurally related known and unknown molecules, focusing on shifted fragment peaks that can indicate which part of the known structure contains a modification. It aggregates fragment-level evidence into atom-level scores that highlight likely modification sites. Current implementations include an interactive GNPS2 web interface, high-throughput workflows, and programmatic components.", "links": { "primary": "https://apps.gnps2.org/modifinder", "web_app": "https://apps.gnps2.org/modifinder", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/modifinder/" }, "resource_types": [ "web_service" ], "functions": { "primary": "annotation_identification", "secondary": [ "spectral_analysis" ], "capabilities": [ "structure_elucidation", "substructure_prediction", "ms2_processing" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app", "api" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.1021/jasms.4c00061", "citation": "Shahneh MRZ, Strobel M, Vitale GA, et al. ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization. J Am Soc Mass Spectrom 35, 2564-2578 (2024).", "type": "primary" } ], "common_uses": [ "Localize likely atom-level positions of a structural modification between a known molecule and a related unknown spectrum.", "Interpret analog-library or molecular-network neighbors with a known reference structure.", "Prioritize structural hypotheses for modified natural products, metabolites, or transformation products.", "Prioritize likely modification regions on a known analog for further structural investigation." ], "scope": { "designed_for": [ "MS/MS pairs representing structurally related molecules where one structure is known and the other differs by a modification." ], "requirements": [ "Paired MS/MS spectra and a known chemical structure for the reference molecule." ], "considerations": [ "Localization is computational evidence and does not establish a complete unknown structure or stereochemistry.", "Performance depends on informative shared/shifted fragments and spectral quality.", "Inspect whether the known and unknown spectra are compatible in polarity and fragmentation conditions before interpreting atom-level scores." ], "not_designed_for": [ "Complete de novo elucidation of an unrelated molecule or confident stereochemical assignment." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser" ], "api_available": true }, "data": { "input_types": [ "spectra", "molecular_structures" ], "output_types": [ "molecular_structures", "report" ], "input_formats": [ "smiles_inchi" ], "output_formats": [ "json" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mol2ccs", "name": "Mol2CCS", "summary": "A research codebase for graph-neural-network prediction of ion-mobility collision cross sections from molecular structure, developed to evaluate model generalizability across chemical domains.", "links": { "primary": "https://github.com/enveda/ccs-prediction", "repository": "https://github.com/enveda/ccs-prediction", "documentation": "https://github.com/enveda/ccs-prediction#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "annotation_identification" ], "capabilities": [ "ccs_prediction" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "Prepared Parquet molecular/adduct tables with optional 3D coordinates" ], "other_output_formats": [ "CCS predictions, HDF5 model files and evaluation artifacts" ] }, "publications": [ { "doi": "10.1186/s13321-024-00899-w", "type": "primary", "citation": "Evaluating the generalizability of graph neural networks for predicting collision cross section. Journal of Cheminformatics (2024)." } ], "common_uses": [ "Predict CCS from molecular structure for research and benchmarking." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-09-09", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "acquisition": { "ms_levels": [ "ms1" ], "strategies": [ "unspecified" ], "ion_mobility": "supported" }, "description": "Mol2CCS supplies graph-neural-network training and prediction code used to study CCS generalization across datasets. Models operate on molecular structures, adducts and optional three-dimensional coordinates, with prepared datasets and evaluation notebooks.", "scope": { "designed_for": [ "A research codebase for graph-neural-network prediction of ion-mobility collision cross sections from molecular structure, developed to evaluate model generalizability across chemical domains." ], "not_designed_for": [ "Calibrating experimental mobility measurements or guaranteeing accurate CCS for chemical domains absent from model training." ], "requirements": [ "The documented Python/Poetry environment, compatible molecular/adduct data and a trained model; reproducing the experiments additionally requires downloading and preparing the licensed METLIN-CCS/CCSBase datasets." ], "considerations": [ "Dataset splits, adduct definitions and structural domain affect evaluation. The repository is a research framework; do not assume a maintained universal pretrained prediction service." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "molnetenhancer", "name": "MolNetEnhancer", "summary": "Combine molecular networking, substructure motifs and chemical classifications into annotated networks.", "description": "MolNetEnhancer integrates outputs from GNPS networking, MS2LDA and supported in-silico annotation tools to help interpret chemical families.", "links": { "primary": "https://ccms-ucsd.github.io/GNPSDocumentation/molnetenhancer/", "documentation": "https://ccms-ucsd.github.io/GNPSDocumentation/molnetenhancer/" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "molecular_networking", "capabilities": [ "network_visualization", "compound_class_prediction" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app", "python_package", "r_package" ], "access": { "model": "source_available", "notes": "Freely available workflow; component tools and chemical classification services have their own terms." }, "data": { "input_types": [ "statistical_results", "compound_list" ], "output_types": [ "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Networking jobs and annotation outputs" ], "other_output_formats": [ "Annotated molecular networks" ] }, "publications": [ { "citation": "Ernst M, Kang KB, Caraballo-Rodríguez AM, et al. MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools. Metabolites 9, E144 (2019).", "type": "primary", "doi": "10.3390/metabo9070144" } ], "common_uses": [ "Add class and motif context to molecular networks.", "Prioritize families for closer spectral inspection." ], "scope": { "designed_for": [ "Add class and motif context to molecular networks." ], "not_designed_for": [ "Converting propagated annotations into confirmed identities." ], "requirements": [ "Compatible molecular-networking results and the identifiers required by the selected annotation inputs." ], "considerations": [ "Inspect the evidence behind each node or family; incomplete or incorrect input annotations can propagate through the overview." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "gnps", "relationship": "complementary" }, { "slug": "ms2lda", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "molnetinvert", "name": "MolNetInvert", "summary": "Create metadata-based molecular networks from GNPS networks or feature tables to explore how metabolites are distributed across sample groups.", "description": "MolNetInvert is a desktop GUI implementing Metadata-Based Molecular Networks (MBMN). It reorganizes processed metabolomics data around selected sample metadata and can simplify network views by collapsing clusters. Metadata connections describe sample-group associations; they are distinct from MS/MS similarity or ion-identity evidence.", "links": { "primary": "https://github.com/AaronJCA/MolNetInvert", "repository": "https://github.com/AaronJCA/MolNetInvert", "documentation": "https://github.com/AaronJCA/MolNetInvert/blob/main/MolNetInvert-UserGuide.pdf" }, "resource_types": [ "software_application" ], "functions": { "primary": "molecular_networking", "secondary": [ "visualization_reporting" ], "capabilities": [ "network_visualization" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "MIT", "notes": "Repository includes an MIT license, Python source and a Windows executable (v1.2). Consult the user guide for setup and input conventions." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "input_formats": [ "csv_tsv", "other" ], "other_input_formats": [ "GNPS GraphML with metadata; optional metadata preference TXT" ], "output_types": [ "report" ], "output_formats": [ "other" ], "other_output_formats": [ "XGMML network for visualization in Cytoscape" ] }, "publications": [ { "type": "primary", "doi": "10.26434/chemrxiv-2024-q4dpr", "citation": "Christiansen JV, Jarmusch SA, Vertot M, Andersen AJC. MolNetInvert: A Tool for Creating Metadata Based Molecular Networks and Their Applications in Large Microbial Datasets. ChemRxiv (2024), version 1. Preprint.", "note": "Direct tool preprint, not a peer-reviewed journal article; no separate peer-reviewed version was verified during this review." } ], "common_uses": [ "Compare metabolite distributions across biological conditions or sample groups.", "Reorganize dense networks and singleton features around metadata." ], "scope": { "designed_for": [ "Downstream exploration of processed MS metabolomics networks and feature tables." ], "not_designed_for": [ "Raw-file preprocessing or confirming compound identities from metadata connections." ], "requirements": [ "GNPS GraphML with appropriate metadata, or a CSV feature table and tab-delimited sample-to-group metadata in the documented format." ], "considerations": [ "Missing or inconsistent sample metadata changes the resulting grouping.", "Collapsing or filtering nodes can hide detail; retain the original network and record settings.", "Input modes and compatibility were checked in v1.2 source; software execution was not tested." ] }, "related_tools": [ { "slug": "gnps", "relationship": "uses", "note": "Accepts documented GNPS classical, feature-based and ion-identity network exports." }, { "slug": "cytoscape", "relationship": "complementary", "note": "Visualize the exported XGMML network." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Metadata checked against the official repository, user guide and tool preprint. Software execution was not tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-15", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "molnotator", "name": "MolNotator", "summary": "A Python package that interprets LC-MS/MS ion relationships and spectral evidence to infer molecule-centric networks rather than networks of individual ion signals.", "links": { "primary": "https://github.com/ZzakB/MolNotator", "repository": "https://github.com/ZzakB/MolNotator", "documentation": "https://github.com/ZzakB/MolNotator#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "molecular_networking", "secondary": [ "annotation_identification" ], "capabilities": [ "ion_identity_networking", "library_matching", "network_visualization" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "compound_list", "statistical_results" ], "other_input_formats": [ "MGF spectra, CSV feature tables and YAML/adduct/reference tables" ], "other_output_formats": [ "CSV node/edge tables and simplified molecular-network results" ] }, "common_uses": [ "Collapse ion-level evidence into predicted molecule nodes.", "Dereplicate candidates using spectral, exact-mass, retention-time, and adduct information." ], "maintenance": { "status": "unclear", "repository_last_activity": "2023-02-02", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MolNotator groups LC-MS/MS ions into putative neutral-molecule nodes using adduct/fragment relationships and combines these with spectral dereplication. Its outputs separate molecule-level hypotheses from redundant measured ions in networks.", "scope": { "designed_for": [ "A Python package that interprets LC-MS/MS ion relationships and spectral evidence to infer molecule-centric networks rather than networks of individual ion signals." ], "not_designed_for": [ "Confirming exact molecular structures from neutral-mass triangulation or handling arbitrary multiply charged ions without method validation." ], "requirements": [ "The documented Python environment, MZmine-style MGF/CSV exports with matching identifiers, parameter/adduct tables and any reference databases used for dereplication; positive and negative data can be combined." ], "considerations": [ "The documented implementation recommends singly charged ions and older dependency versions. Adduct rules and upstream feature quality affect inferred molecule nodes; the cited method remains a preprint." ] }, "publications": [ { "doi": "10.1101/2021.12.21.473622", "type": "primary", "citation": "From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics. bioRxiv preprint (2021). Preprint; not peer reviewed." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mona", "name": "MoNA", "summary": "Search aggregated mass spectra and download reference libraries for local annotation workflows.", "description": "MassBank of North America brings spectra, structures and metadata together in a searchable resource. Web queries and documented REST queries help retrieve records by metadata, while downloadable collections support use in other tools.", "links": { "primary": "https://massbank.us/", "homepage": "https://massbank.us/", "documentation": "https://mona.fiehnlab.ucdavis.edu/documentation/query" }, "resource_types": [ "spectral_library", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "metadata_contextualization", "provenance_tracking" ] }, "platforms": [ "lc_ms", "gc_ms" ], "interfaces": [ "web_app", "api" ], "access": { "model": "free_web_service", "notes": "Public browsing and downloads are available. Check the originating collection and applicable reuse terms rather than assuming a single license covers all aggregated records." }, "data": { "input_types": [ "compound_list", "sample_metadata" ], "output_types": [ "spectra", "spectral_library" ], "other_input_formats": [ "Compound and spectrum metadata query" ], "output_formats": [ "json", "msp", "sdf" ] }, "common_uses": [ "Retrieve spectra using compound identifiers or acquisition metadata.", "Download a library subset appropriate for an annotation tool." ], "scope": { "designed_for": [ "Reference-library discovery and metadata-based retrieval." ], "not_designed_for": [ "A repository for a complete study and its raw sample files." ], "requirements": [ "Select the relevant spectral collection and export format; use pagination for programmatic queries." ], "considerations": [ "Inspect record origin and evidence type. Overlap between source collections means aggregated records are not necessarily independent reference measurements." ] }, "publications": [], "related_tools": [ { "slug": "massbank", "relationship": "complementary" }, { "slug": "metfrag", "relationship": "complementary" } ], "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review on 7 September 2026 against official documentation and the sources listed here. Scope and practical guidance are editorial synthesis; software execution and service availability were not validated. No dedicated MoNA resource paper was confirmed in this pass; official query and download documentation support this entry. Cite the original reference data used." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-07", "last_editorial_review": "2026-09-07" }, "aliases": [ "MassBank of North America" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mrmquant", "name": "MRMQuant", "summary": "Quantify targeted metabolites from MRM chromatograms.", "description": "MRMQuant provides a MATLAB graphical interface for MRM peak integration, calibration-based quantification and manual review of targeted measurements.", "links": { "primary": "https://github.com/kslynn128171/MRMQuant", "repository": "https://github.com/kslynn128171/MRMQuant", "documentation": "https://github.com/kslynn128171/MRMQuant#readme" }, "platforms": [ "lc_ms" ], "interfaces": [ "desktop_gui", "matlab" ], "resource_types": [ "software_application" ], "functions": { "primary": "quantification", "capabilities": [ "calibration_curve", "peak_detection" ] }, "access": { "model": "source_available", "notes": "Source and distributions are publicly available. An explicit software license was not identified in the reviewed repository; the paper describes the tool as open source, but reuse terms should be confirmed." }, "data": { "input_types": [ "spectra", "compound_list" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "MRM mzML or text files and a target transition/retention-time list" ], "other_output_formats": [ "Quantitation results and reloadable MATLAB MAT analysis files" ] }, "common_uses": [ "Quantify targeted metabolites from MRM chromatograms." ], "scope": { "designed_for": [ "MRMQuant provides a MATLAB graphical interface for MRM peak integration, calibration-based quantification and manual review of targeted measurements." ], "not_designed_for": [ "Untargeted structure discovery from arbitrary MS/MS data." ], "requirements": [ "MRM measurements and target precursor/product m/z, retention times and tolerances.", "Known-concentration standards for absolute quantification; suitable controls for optional correction." ], "considerations": [ "Review integration, coelution and calibration fits; automated results do not replace assay validation." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c02462", "type": "primary", "citation": "MRMQuant: Automated MRM Data Quantitation for Large-Scale Targeted Metabolomics Analysis. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" }, "analysis_types": [ "targeted" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms-bart", "name": "MS-BART", "summary": "MS-BART models molecular representations and mass-spectral information with a unified language-model workflow for structure elucidation.", "links": { "primary": "https://github.com/OpenDFM/MS-BART", "repository": "https://github.com/OpenDFM/MS-BART", "documentation": "https://github.com/OpenDFM/MS-BART#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "source_available", "notes": "Public research code and separately hosted weights/data; no repository-wide license grant was established from reviewed metadata. Consult artifact and dependency terms separately." }, "technical": { "operating_systems": [ "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures" ], "other_input_formats": [ "Prepared spectral/fingerprint TSV data and module-specific MGF inputs" ], "other_output_formats": [ "Generated molecular candidates and training/evaluation artifacts" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-09-30", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MS-BART models molecular representations and mass-spectral information with a unified language-model workflow for structure elucidation. Its research pipeline includes molecular pretraining, spectral fine-tuning, chemical-feedback alignment and candidate generation.", "scope": { "designed_for": [ "MS-BART models molecular representations and mass-spectral information with a unified language-model workflow for structure elucidation." ], "not_designed_for": [ "A turnkey raw-MS identification application or guaranteed correct structures for spectra outside the model domain." ], "requirements": [ "The documented Python 3.9 environment, downloaded checkpoints/data and fingerprint preprocessing resources; review configuration paths and evaluation-script settings before use." ], "considerations": [ "README documents path and beam-variable issues in evaluation scripts. Preserve evaluation splits and distinguish predicted fingerprints and generated structures from experimental confirmation." ] }, "common_uses": [ "MS-BART models molecular representations and mass-spectral information with a unified language-model workflow for structure elucidation." ], "publications": [ { "doi": "10.48550/arXiv.2510.20615", "citation": "Han et al. MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation. arXiv:2510.20615 (2025). Authors identify the associated conference as NeurIPS 2025.", "type": "primary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms-dial", "name": "MS-DIAL", "summary": "A graphical and command-line workbench for untargeted metabolomics and lipidomics data processing across multiple chromatography and mass spectrometry platforms.", "links": { "primary": "https://systemsomicslab.github.io/compms/msdial/main", "homepage": "https://systemsomicslab.github.io/compms/msdial/main", "repository": "https://github.com/systemsomicslab/MsdialWorkbench", "documentation": "https://systemsomicslab.github.io/msdial5tutorial/" }, "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "secondary": [ "normalization", "annotation_identification", "spectral_analysis", "statistics", "visualization_reporting" ], "capabilities": [ "peak_detection", "spectral_deconvolution", "retention_time_alignment", "gap_filling", "adduct_isotope_grouping", "library_matching", "ms2_processing", "multivariate_statistics" ] }, "platforms": [ "lc_ms", "gc_ms", "ce_ms", "direct_infusion_ms", "maldi_ms" ], "analysis_types": [ "untargeted", "lipidomics", "stable_isotope_tracing", "metabolite_profiling" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "source_available", "notes": "The software, source, and associated libraries are publicly accessible; users should confirm the license and terms that apply to the specific component and version they use." }, "technical": { "operating_systems": [ "windows" ], "programming_languages": [ "C#" ], "api_available": false }, "data": { "input_types": [ "raw_instrument_data", "spectra", "spectral_library" ], "output_types": [ "feature_table", "spectra", "compound_list", "statistical_results" ], "input_formats": [ "vendor_raw", "mzml", "netcdf", "msp" ], "output_formats": [ "csv_tsv", "msp", "mgf", "mztab_m" ] }, "publications": [ { "doi": "10.1038/s41587-020-0531-2", "type": "methods", "citation": "Tsugawa H, Ikeda K, Takahashi M, et al. A lipidome atlas in MS-DIAL 4. Nat Biotechnol 38, 1159-1163 (2020).", "note": "Introduces MS-DIAL 4 alongside the lipidome atlas; retained as a direct software update." }, { "doi": "10.1038/nmeth.3393", "citation": "Tsugawa H, Cajka T, Kind T, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12, 523-526 (2015).", "type": "primary" } ], "common_uses": [ "Process untargeted LC-MS, GC-MS, and lipidomics datasets through a graphical workflow.", "Deconvolute data-independent MS/MS spectra and match features against spectral libraries.", "Export aligned abundances and representative spectra for downstream analysis while preserving feature IDs." ], "related_tools": [ { "slug": "mzmine", "relationship": "alternative" }, { "slug": "gnps", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-21" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-21", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "Broad untargeted metabolomics and lipidomics processing through a graphical workflow across several chromatography and MS acquisition modes.", "Datasets requiring deconvolution and interpretation of data-independent or all-ion MS/MS signals." ], "requirements": [ "Users should confirm current operating-system support and input-format compatibility for the version they install." ], "considerations": [ "Large libraries and highly permissive annotation settings can increase candidate identifications; confidence levels and library provenance should be reported.", "For lipidomics, report the structural level supported by the fragmentation evidence; review deconvolution and retention information for ambiguous features." ], "not_designed_for": [ "Treating all library annotations as equally resolved molecular structures." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia", "all_ion" ], "ion_mobility": "supported" }, "description": "MS-DIAL is a metabolomics and lipidomics workbench designed for broad-spectrum processing of chromatography–mass-spectrometry datasets. It combines peak detection, deconvolution, alignment, spectral annotation, normalization, statistics, and visualization, with particular support for data-independent MS/MS processing and lipid annotation.", "aliases": [ "MSDIAL", "MS Dial" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms-finder", "name": "MS-FINDER", "summary": "Explore formula and structure candidates using fragmentation rules and EI-MS or MS/MS library searches.", "description": "MS-FINDER combines formula prediction, fragment interpretation and structure ranking. Its graphical and console interfaces support spectral-library searching and in silico prediction. It operates on prepared spectra, making it a companion to upstream processing tools.", "links": { "primary": "https://systemsomicslab.github.io/compms/msfinder/main.html", "homepage": "https://systemsomicslab.github.io/compms/msfinder/main.html", "documentation": "https://systemsomicslab.github.io/compms/msfinder/consoleapp.html" }, "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "capabilities": [ "molecular_formula_prediction", "structure_elucidation", "library_matching" ] }, "platforms": [ "lc_ms", "gc_ms" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "source_available", "notes": "Software and source are publicly accessible. Check the license for the chosen distribution and the separate terms of bundled spectral libraries." }, "data": { "input_types": [ "spectra", "spectral_library" ], "output_types": [ "compound_list", "report" ], "input_formats": [ "msp" ], "other_input_formats": [ "MS-FINDER MAT (spectral text format)" ], "other_output_formats": [ "MS-FINDER ASCII result files" ] }, "common_uses": [ "Rank candidate formulas and structures for unknown spectra.", "Search EI-MS or MS/MS libraries through the GUI or a batch console job." ], "scope": { "designed_for": [ "Interpretation of prepared small-molecule mass spectra." ], "not_designed_for": [ "Replacing raw-data peak detection and alignment." ], "requirements": [ "Prepare MSP or MS-FINDER MAT files and configure precursor and mass-error settings. Check distribution requirements before installation." ], "considerations": [ "Fragmentation rules and library coverage constrain the result; review ambiguous candidates and retain the parameter file." ] }, "publications": [ { "doi": "10.1021/acs.analchem.6b00770", "citation": "Tsugawa H, Kind T, Nakabayashi R, et al. Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software. Anal Chem 88, 7946-7958 (2016).", "type": "primary" } ], "related_tools": [ { "slug": "ms-dial", "relationship": "complementary" }, { "slug": "metfrag", "relationship": "alternative" }, { "slug": "massbank", "relationship": "uses" } ], "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review on 7 September 2026 against official documentation and the sources listed here. Scope and practical guidance are editorial synthesis; software execution and service availability were not validated." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "aliases": [ "MSFINDER" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms-mint", "name": "MS-MINT", "aliases": [], "summary": "MS-MINT (MS-Metabolomics Integrator) is a Python application for processing, analyzing, and visualizing large liquid chromatography-mass spectrometry (LC-MS) metabolomics datasets", "links": { "primary": "https://github.com/LewisResearchGroup/ms-mint-app2", "repository": "https://github.com/LewisResearchGroup/ms-mint-app2" }, "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "secondary": [ "visualization_reporting", "statistics" ], "capabilities": [] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "python_package" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.6c01083", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-18" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/43", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389", "notes": "This software addresses traditional shortcomings in metabolomics data analysis by using an end-to-end workflow based on the region-of-interest (ROI) approach modeled after NMR software. \nIn MS-MINT, ROIs are defined as windows of retention time and m/z that are extracted from MS1 spectra.\nEach metabolite is therefore defined by a specific ROI, which can then be extracted from each spectrum and visualized or analyzed by data processing algorithms." }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-18", "updated_at": "2026-09-18", "last_verified": "2026-09-18" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms-net", "name": "MS-Net", "aliases": [ "MSNet", "MS-Net" ], "summary": "MS-Net combines annotation levels from both mass spectral libraries and in silico tools such as MS-Finder or Sirius-CSI to build a comprehensive candidate chemical space. Then, MS-Net prioritizes annotation using the molecular network and Tanimoto-based similarity with optional biosource reranking. Additionnaly it can combine positive and negative acquisition modes and add metadata to all annotated features, such as chemical ontology or database identifiers.", "links": { "primary": "https://forge.inrae.fr/metatoul/equipe-agromix/ms-net", "homepage": "https://forge.inrae.fr/metatoul/equipe-agromix/ms-net" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "annotation_identification", "secondary": [ "molecular_networking" ], "capabilities": [] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "knime" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.6c01026", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-16" }, "credits": [ { "name": "Guillaume Marti", "role": "developer", "orcid": "0000-0002-6321-9005" } ], "provenance": { "submitted_by": "developer", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/38", "developer_verified": false, "submitter_name": "Guillaume Marti", "public_attribution_consent": true, "submitter_orcid": "0000-0002-6321-9005", "notes": "MS-Net is compatible with output files from MZmine, Sirius, MS-Dial and MS-Finder" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-16", "updated_at": "2026-09-16", "last_verified": "2026-09-16" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms-ridd", "name": "MS-RIDD", "summary": "An open-source Python application for interpreting radical-induced dissociation tandem mass spectra, with current support focused on oxygen attachment dissociation for detailed lipid structural annotation.", "links": { "primary": "https://github.com/Metabolomics-CompMS/MS-RIDD", "repository": "https://github.com/Metabolomics-CompMS/MS-RIDD", "documentation": "https://github.com/Metabolomics-CompMS/MS-RIDD#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "secondary": [ "spectral_analysis" ], "capabilities": [ "structure_elucidation", "ms2_processing" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "license": "LGPL-3.0-only (source); CC-BY-4.0 (assembly)", "notes": "The README specifies LGPL-3.0 for source code and CC-BY-4.0 for the distributed assembly. Keep those scopes distinct when obtaining or redistributing the software." }, "technical": { "operating_systems": [ "windows" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra", "feature_table" ], "output_types": [ "compound_list", "report" ], "input_formats": [ "mzml", "csv_tsv" ], "other_output_formats": [ "Pickle project files with analysis tables and CID/OAD structural evidence" ] }, "publications": [ { "doi": "10.1038/s42004-022-00778-1", "type": "primary", "citation": "Computational mass spectrometry accelerates C = C position-resolved untargeted lipidomics using oxygen attachment dissociation. Communications Chemistry (2022)." } ], "common_uses": [ "Interpret OAD-MS/MS spectra for detailed lipid structural annotation.", "Consume MS-DIAL peak-list and alignment exports as inputs to a lipid-structure workflow." ], "maintenance": { "status": "unclear", "repository_last_activity": "2022-06-22", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Lipid structural interpretation using radical-induced dissociation, currently documented for OAD-MS/MS." ], "requirements": [ "The documented Python environment, OAD-MS/MS mzML files and MS-DIAL peak/alignment exports with CID-derived lipid annotations. Batch work also requires the associated PeakID and per-sample peak lists." ], "considerations": [ "Double-bond assignments depend on observed diagnostic fragments and the starting lipid annotations; unresolved candidates require further evidence." ], "not_designed_for": [ "General metabolite identification from ordinary CID spectra without the specialized OAD evidence." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "targeted_msms", "dda" ], "ion_mobility": "unspecified" }, "description": "MS-RIDD interprets oxygen-attachment-dissociation spectra to prioritize lipid double-bond positions. It combines OAD-MS/MS with MS-DIAL lipid annotations derived from CID measurements and supports single-sample and batch projects." }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms2decide", "name": "MS2DECIDE", "summary": "Combine GNPS, ISDB-LOTUS and SIRIUS annotations into scores for natural-product feature prioritization.", "description": "MS2DECIDE is a Python library implementing an expert-informed decision model. Its K-estimation workflow aggregates complementary annotation results and exports scored features for follow-up investigation.", "links": { "primary": "https://github.com/MejriY/MS2DECIDE", "repository": "https://github.com/MejriY/MS2DECIDE", "documentation": "https://github.com/MejriY/MS2DECIDE" }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "identification_confidence" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "MIT license verified in the official repository. Software was not installed or execution-tested; dependencies and external services have their own requirements." }, "data": { "input_types": [ "feature_table", "spectra", "compound_list" ], "output_types": [ "feature_table", "statistical_results" ], "input_formats": [ "csv_tsv", "mgf" ], "output_formats": [ "csv_tsv" ], "other_output_formats": [ "Filtered dataframe and TSV file with K-values" ] }, "common_uses": [ "Prioritize natural-product features using multiple annotation sources and an explicit decision model." ], "scope": { "designed_for": [ "Combine GNPS, ISDB-LOTUS and SIRIUS annotations into scores for natural-product feature prioritization." ], "not_designed_for": [ "Replacing experimental confirmation of compound identity.", "Raw-data feature detection or chromatographic alignment." ], "requirements": [ "A quantitative feature table, corresponding MGF spectra and compatible SIRIUS identification results with matching feature identifiers.", "A supported Python environment, GNPS account and network access for the documented workflow.", "The documentation describes SIRIUS 6 and MZmine FBMN-compatible exports; check current format compatibility before use." ], "considerations": [ "K-values reflect the decision model and available annotations, not probabilities of correct identification or proof of chemical novelty.", "Results depend on reference coverage and agreement between annotation sources. Inspect spectra and investigate prioritized features experimentally." ] }, "publications": [ { "doi": "10.1002/cmtd.202400088", "type": "primary", "citation": "MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization. Chemistry–Methods (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake reviewed against the primary publication, official repository documentation and license on 2026-10-05. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-05" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-05", "updated_at": "2026-10-05", "last_verified": "2026-10-05", "last_editorial_review": "2026-10-05" }, "related_tools": [ { "slug": "gnps", "relationship": "integration", "note": "The documented workflow obtains GNPS annotation evidence." }, { "slug": "sirius", "relationship": "integration", "note": "Consumes compatible SIRIUS annotation results." }, { "slug": "mzmine", "relationship": "integration", "note": "The README describes compatible MZmine FBMN-format exports, not an embedded plugin." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms2deepscore", "name": "MS2DeepScore", "summary": "Estimate molecular structural similarity from pairs of tandem mass spectra.", "description": "MS2DeepScore uses a Siamese neural network to predict fingerprint-based similarity and generate spectral embeddings. Model versions differ in supported ionization modes.", "links": { "primary": "https://github.com/matchms/ms2deepscore", "documentation": "https://github.com/matchms/ms2deepscore", "repository": "https://github.com/matchms/ms2deepscore" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "library_matching" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "notes": "Apache-2.0 software; trained models and training datasets may carry additional terms." }, "data": { "input_types": [ "spectra" ], "output_types": [ "statistical_results" ], "input_formats": [ "mgf", "msp" ], "output_formats": [ "other" ], "other_output_formats": [ "Predicted similarity matrices and embeddings" ] }, "publications": [ { "citation": "Huber F, van der Burg S, van der Hooft JJJ, et al. MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra. J Cheminform 13, 84 (2021).", "type": "primary", "doi": "10.1186/s13321-021-00558-4" } ], "common_uses": [ "Prioritize potentially related molecules from MS/MS spectra.", "Explore spectral collections through learned embeddings." ], "scope": { "designed_for": [ "Prioritize potentially related molecules from MS/MS spectra." ], "not_designed_for": [ "Interpreting predicted similarity as an identification probability." ], "requirements": [ "Python, cleaned spectra and a compatible model checkpoint." ], "considerations": [ "Use a model appropriate to the ion mode and chemical domain; report model version and preprocessing." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "matchms", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms2lda", "name": "MS2LDA", "summary": "Find recurring patterns of fragments and neutral losses across tandem mass spectra.", "description": "MS2LDA represents recurring fragmentation patterns as Mass2Motifs, which can support substructure interpretation across a dataset. The project now provides MS2LDA 2.0 and an updated visualization interface.", "links": { "primary": "https://ms2lda.org/", "documentation": "https://ms2lda.org/" }, "resource_types": [ "software_application" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "substructure_prediction" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "web_app" ], "access": { "model": "source_available", "notes": "Code and visualization services are public; consult the implementation license and hosted-service limits." }, "data": { "input_types": [ "spectra" ], "output_types": [ "report", "statistical_results" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "MS/MS peak lists" ], "other_output_formats": [ "Mass2Motifs, associations and visualizations" ] }, "publications": [ { "citation": "van der Hooft JJ, Wandy J, Barrett MP, et al. Topic modeling for untargeted substructure exploration in metabolomics. Proc Natl Acad Sci U S A 113, 13738-13743 (2016).", "type": "primary", "doi": "10.1073/pnas.1608041113" } ], "common_uses": [ "Discover shared fragmentation motifs.", "Explore motif occurrences across molecular families." ], "scope": { "designed_for": [ "Discover shared fragmentation motifs." ], "not_designed_for": [ "Treating every motif as a uniquely established substructure." ], "requirements": [ "Processed MS/MS spectra and a workflow compatible with the current MS2LDA implementation." ], "considerations": [ "Motif interpretation depends on spectral coverage and annotation evidence; the official site flags retirement of its legacy interface." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "gnps", "relationship": "complementary" }, { "slug": "molnetenhancer", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms2lipid", "name": "MS2Lipid", "summary": "A Python package for machine-learning prediction of lipid subclasses from tandem mass spectra using curated lipid MS/MS reference data.", "links": { "primary": "https://github.com/systemsomicslab/ms2lipid", "repository": "https://github.com/systemsomicslab/ms2lipid", "documentation": "https://github.com/systemsomicslab/ms2lipid#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "compound_class_prediction", "ms2_processing" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "lipidomics" ], "interfaces": [ "python_package", "notebook" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ], "package_managers": [ "PyPI" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "other_input_formats": [ "Spectrum peak arrays and metadata via the documented Python/Colab interface" ], "other_output_formats": [ "Predicted lipid subclasses and associated classifier results" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-11-18", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MS2Lipid predicts lipid subclasses from tandem mass spectra using a trained machine-learning model. It provides an orthogonal class-level annotation signal for lipidomics and can be used through Python or the supplied Colab example.", "scope": { "designed_for": [ "A Python package for machine-learning prediction of lipid subclasses from tandem mass spectra using curated lipid MS/MS reference data." ], "not_designed_for": [ "Full lipid molecular-species identification, acyl-chain assignment or structural confirmation from a subclass label alone." ], "requirements": [ "The documented compatible Python environment, trained model and spectra/precursor metadata arranged according to the example notebook's input interface." ], "considerations": [ "Class coverage and domain differences from the curated training spectra affect results. Inspect mixed/chimeric spectra and score interpretation before using labels in downstream networks." ] }, "common_uses": [ "A Python package for machine-learning prediction of lipid subclasses from tandem mass spectra using curated lipid MS/MS reference data." ], "publications": [ { "doi": "10.3390/metabo14110602", "type": "primary", "citation": "MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data. Metabolites (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "access": { "model": "free_academic", "notes": "Official license permits academic/nonprofit noncommercial internal research, with restrictions on redistribution and derivatives. Not an unrestricted open-source license." }, "common_uses": [ "Plan precursor acquisition schedules from LC-MS survey features." ], "data": { "input_types": [ "feature_table" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "Prepared survey-feature table; optional MS1 mzML or mzTab for curve mode" ], "other_output_formats": [ "Text or CSV acquisition paths and a planning log" ] }, "description": "MS2Planner computes acquisition paths from feature masses, retention times and signals. Companion scripts translate these paths into instrument inclusion lists.", "functions": { "primary": "data_acquisition", "capabilities": [ "blank_filtering" ] }, "interfaces": [ "command_line" ], "links": { "primary": "https://github.com/mohimanilab/MS2Planner", "repository": "https://github.com/mohimanilab/MS2Planner", "documentation": "https://github.com/mohimanilab/MS2Planner#readme" }, "maintenance": { "last_checked": "2026-10-21", "status": "unclear" }, "name": "MS2Planner", "platforms": [ "lc_ms" ], "provenance": { "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark.", "submitted_by": "curator" }, "publications": [ { "doi": "10.1093/bioinformatics/btab279", "type": "primary", "citation": "MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition. Bioinformatics (2021)." } ], "resource_types": [ "workflow_pipeline" ], "scope": { "designed_for": [ "MS2Planner computes acquisition paths from feature masses, retention times and signals. Companion scripts translate these paths into instrument inclusion lists." ], "not_designed_for": [ "Real-time instrument control or retrospective metabolite identification." ], "requirements": [ "Survey features with sample/background information and a configured Python environment.", "Instrument-compatible inclusion-list export, repeat injections and reproducible chromatography." ], "considerations": [ "Timing and isolation parameters must match the instrument; planned coverage does not guarantee usable MS/MS spectra." ] }, "slug": "ms2planner", "status": { "created_at": "2026-10-21", "entry": "published", "last_editorial_review": "2026-10-21", "last_verified": "2026-10-21", "review": "editorially_reviewed", "updated_at": "2026-10-21" }, "summary": "Plan precursor acquisition schedules from LC-MS survey features.", "analysis_types": [ "untargeted" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ms2query", "name": "MS2Query", "summary": "MS/MS analogue-search tool that combines learned spectral similarity and precursor-mass information to retrieve structurally related reference compounds.", "links": { "primary": "https://gnps2.org/workflowinput?workflowname=ms2query_nextflow_workflow", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/ms2query_doc/", "repository": "https://github.com/iomega/ms2query" }, "resource_types": [ "programming_package", "workflow_pipeline", "web_service" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "library_matching" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app", "python_package", "command_line" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "The Python software is distributed under Apache-2.0. For the hosted workflow: GNPS2 is openly available to the academic community. The current GNPS2 documentation asks industry users to contact the maintainers or UC Riverside Technology Transfer regarding organizational access. Workflow submission requires a GNPS2 account." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review of official documentation, repository and the direct tool publication on 2026-09-10. No analysis job, independent benchmark or developer verification was performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list", "statistical_results" ], "input_formats": [ "mgf", "msp", "mzml", "mzxml", "json" ], "other_input_formats": [ "Universal Spectrum Identifiers (USIs), where supported by the selected interface" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "citation": "de Jonge NF, Louwen JJR, Chekmeneva E, et al. MS2Query: reliable and scalable MS2 mass spectra-based analogue search. Nature Communications 14, 1752 (2023).", "type": "primary", "doi": "10.1038/s41467-023-37446-4", "pmid": "36990978" } ], "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Available as a GNPS2 workflow." }, { "slug": "spec2vec", "relationship": "integration", "note": "Uses learned spectral similarity concepts from Spec2Vec." }, { "slug": "ms2deepscore", "relationship": "integration", "note": "MS2DeepScore predictions contribute to candidate ranking." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "description": "MS2Query searches reference MS/MS spectra for both exact-match candidates and structurally related analogues. It combines Spec2Vec, MS2DeepScore and precursor information to rank library candidates, with a Python implementation and a hosted GNPS2 workflow.", "common_uses": [ "Find plausible structural relatives when an exact library match is unavailable.", "Prioritize spectra for closer structural investigation." ], "scope": { "designed_for": [ "Library searching of small-molecule MS/MS spectra, including analogue retrieval." ], "not_designed_for": [ "De novo structure generation or conclusive identification of an unknown molecule.", "Chromatographic feature detection, alignment or quantification." ], "requirements": [ "MS/MS spectra with precursor information and the correct ionization mode.", "Compatible reference-library and model files; hosted jobs require uploaded spectra and a GNPS2 account." ], "considerations": [ "An analogue is a related reference molecule, not necessarily the measured compound.", "Filter results by model score and inspect precursor differences; class labels describe the proposed library molecule.", "Results depend on reference-library coverage and model compatibility. Record the library, model and software versions." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "msbert", "name": "MSBERT", "summary": "Embed tandem spectra for similarity scoring and reference-library matching.", "description": "MSBERT learns MS/MS embeddings using masked and contrastive learning. Examples compute spectrum similarities and integrate the scoring method into matchms.", "links": { "primary": "https://github.com/zhanghailiangcsu/MSBERT#readme", "repository": "https://github.com/zhanghailiangcsu/MSBERT", "documentation": "https://github.com/zhanghailiangcsu/MSBERT#readme" }, "platforms": [ "lc_ms" ], "interfaces": [ "command_line", "web_app" ], "resource_types": [ "software_application" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "library_matching" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "MIT license verified in the official source repository. Training datasets and model downloads may have separate terms. No execution or independent performance benchmark." }, "data": { "input_types": [ "spectra", "spectral_library" ], "output_types": [ "report" ], "other_input_formats": [ "MSP tandem spectra and trained MSBERT weights" ], "other_output_formats": [ "Embedding vectors and pairwise spectrum similarity scores" ] }, "common_uses": [ "Embed tandem spectra for similarity scoring and reference-library matching." ], "scope": { "designed_for": [ "MSBERT learns MS/MS embeddings using masked and contrastive learning. Examples compute spectrum similarities and integrate the scoring method into matchms." ], "not_designed_for": [ "De novo structure generation or evidence of identity from similarity alone." ], "requirements": [ "Python/PyTorch environment and pretrained model; reference spectra for library search." ], "considerations": [ "Training and evaluation use curated GNPS data; assess performance for the instrument and chemistry of your study.", "A documented Streamlit interface is available; hosted service operation was not tested." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c02426", "type": "primary", "citation": "MSBERT: Embedding Tandem Mass Spectra into Chemically Rational Space by Mask Learning and Contrastive Learning. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "related_tools": [ { "slug": "matchms", "relationship": "integration", "note": "Official examples integrate MSBERT similarity scoring into matchms." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "msbuddy", "name": "BUDDY / msbuddy", "summary": "Generate and rank molecular formula annotations for small-molecule mass-spectrometry queries.", "description": "msbuddy implements BUDDY formula analysis using precursor information and optional isotope patterns and MS/MS evidence. It supports both ionization polarities.", "links": { "primary": "https://github.com/Philipbear/msbuddy", "documentation": "https://github.com/Philipbear/msbuddy", "repository": "https://github.com/Philipbear/msbuddy" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "molecular_formula_prediction" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "open_source", "notes": "Apache-2.0 software." }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "citation": "Xing S, Shen S, Xu B, et al. BUDDY: molecular formula discovery via bottom-up MS/MS interrogation. Nat Methods 20, 881-890 (2023).", "type": "primary", "doi": "10.1038/s41592-023-01850-x" } ], "common_uses": [ "Annotate formulas for a batch of MGF spectra.", "Inspect fragment subformula assignments for a proposed precursor formula." ], "scope": { "designed_for": [ "Annotate formulas for a batch of MGF spectra." ], "not_designed_for": [ "Distinguishing all structural isomers that share a formula." ], "requirements": [ "At minimum an m/z value and polarity; accurate precursor and fragmentation data improve the available evidence." ], "considerations": [ "Element constraints, adduct assumptions and mass tolerances affect candidates. The documented scope targets molecules below 1500 Da." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "sirius", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "msconvert", "name": "msconvert", "summary": "Convert mass-spectrometry files into open formats for downstream analysis.", "description": "Part of ProteoWizard, msconvert provides command-line conversion and optional spectral filters. MSConvertGUI offers a graphical route through the same ecosystem.", "links": { "primary": "https://www.proteowizard.org/tools/msconvert.html", "documentation": "https://www.proteowizard.org/tools/msconvert.html" }, "resource_types": [ "software_application" ], "functions": { "primary": "data_conversion", "capabilities": [ "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "command_line", "desktop_gui" ], "access": { "model": "open_source", "notes": "ProteoWizard is open source; vendor reader components have their own distribution and platform terms." }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "spectra" ], "input_formats": [ "vendor_raw", "mzml", "mzxml" ], "output_formats": [ "mzml", "mzxml", "mgf" ] }, "publications": [ { "citation": "Kessner D, Chambers M, Burke R, et al. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534-2536 (2008).", "type": "primary", "doi": "10.1093/bioinformatics/btn323", "note": "Official ProteoWizard platform citation covering msconvert; not a standalone msconvert paper." } ], "common_uses": [ "Prepare mzML files for MZmine, XCMS or other compatible processors.", "Automate conversion across an acquisition batch." ], "scope": { "designed_for": [ "Prepare mzML files for MZmine, XCMS or other compatible processors." ], "not_designed_for": [ "Producing an aligned metabolite abundance table by conversion alone." ], "requirements": [ "A supported reader for the instrument format; some vendor readers require Windows." ], "considerations": [ "Record centroiding and filtering choices, and retain original files: conversion settings can alter downstream results." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "mzmine", "relationship": "complementary" }, { "slug": "xcms", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "msentropy", "name": "MSEntropy", "summary": "An open-source multi-language implementation of spectral entropy, entropy similarity, and flash entropy search for comparing and querying small-molecule mass spectra.", "links": { "primary": "https://msentropy.readthedocs.io/", "homepage": "https://msentropy.readthedocs.io/", "repository": "https://github.com/YuanyueLi/MSEntropy", "documentation": "https://msentropy.readthedocs.io/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "secondary": [ "annotation_identification", "reference_data_search" ], "capabilities": [ "library_matching", "ms2_processing" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "r_package" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 implementation; reference-library availability and licensing are separate from the software license. Language interfaces provide different subsets of functionality." }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "programming_languages": [ "Python", "R", "C", "C++" ] }, "data": { "input_types": [ "spectra", "spectral_library" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "In-memory m/z-intensity arrays and library spectrum dictionaries" ], "other_output_formats": [ "Entropy values, similarity arrays and indexed-search scores" ] }, "publications": [ { "doi": "10.1038/s41592-021-01331-z", "type": "methods", "citation": "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nature Methods (2021)." }, { "doi": "10.1038/s41592-023-02012-9", "type": "primary", "citation": "Flash entropy search to query all mass spectral libraries in real time. Nature Methods (2023)." } ], "common_uses": [ "Calculate spectral entropy and entropy-based similarity between MS/MS spectra.", "Search spectral libraries with flash entropy search." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-05", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Entropy-based spectral comparison and high-speed library searching." ], "considerations": [ "Cleaning, intensity normalization and fragment tolerances affect scores. Confirm that candidate precursor/adduct conditions and reference spectra are appropriate." ], "not_designed_for": [ "Raw chromatographic peak extraction or confirmation of identity solely from an entropy score." ], "requirements": [ "Centroid m/z-intensity peak arrays and appropriate mass tolerances; library search additionally requires a prepared reference library and precursor metadata." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "description": "MSEntropy implements spectral entropy and entropy-based similarity calculations, with Flash Entropy Search for indexed library comparisons. Python provides the indexed search interface, while R and C/C++ interfaces expose entropy and comparison functions." }, { "$schema": "../../schemas/tool.schema.json", "slug": "msgo", "name": "MSGO", "summary": "A research implementation for de novo molecular-structure generation from mass-spectral evidence using pseudodata-trained machine learning.", "links": { "primary": "https://github.com/aaronma2020/MSGO", "repository": "https://github.com/aaronma2020/MSGO", "documentation": "https://github.com/aaronma2020/MSGO#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures" ], "other_input_formats": [ "Example-structured tandem-spectrum CSVs and trained model checkpoints" ], "other_output_formats": [ "CSV of ranked generated molecular-structure candidates" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-04-07", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "A research implementation for de novo molecular-structure generation from mass-spectral evidence using pseudodata-trained machine learning." ], "not_designed_for": [ "A universal structure generator with validated coverage of all chemical classes or confirmed identifications from top-ranked SMILES alone." ], "requirements": [ "The documented Python/PyTorch environment, matching model weights and correctly formatted spectrum CSVs; use the polarity and model appropriate to the chemical domain." ], "considerations": [ "Pseudodata and restricted PFAS/lipid training domains limit transfer. Some model/data assets use external download services; inspect accessibility and precursor/formula consistency before interpreting generated candidates." ] }, "description": "MSGO generates candidate molecular structures from tandem spectra with a model trained using simulated spectrum-structure pairs. The published implementation provides PFAS and lipid model routes with beam-search candidate outputs.", "common_uses": [ "A research implementation for de novo molecular-structure generation from mass-spectral evidence using pseudodata-trained machine learning." ], "publications": [ { "doi": "10.1038/s42256-025-01140-5", "type": "primary", "citation": "Pseudodata-based molecular structure generator to reveal unknown chemicals. Nature Machine Intelligence (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mshub-gc", "name": "MSHub-GC", "summary": "GC-MS processing workflow that aligns chromatographic data and deconvolves overlapping signals into spectra and quantitative tables for downstream GNPS analysis.", "links": { "primary": "https://gnps2.org/workflowinput?workflowname=mshubgc_workflow", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/mshub-gc_doc/", "repository": "https://github.com/Wang-Bioinformatics-Lab/mshub-gc_workflow" }, "resource_types": [ "workflow_pipeline", "web_service" ], "functions": { "primary": "preprocessing", "secondary": [ "molecular_networking", "spectral_analysis" ], "capabilities": [ "spectral_deconvolution" ] }, "platforms": [ "gc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "GNPS2 is openly available to the academic community. The current GNPS2 documentation asks industry users to contact the maintainers or UC Riverside Technology Transfer regarding organizational access. Workflow submission requires a GNPS2 account. The linked workflow repository is public; this hosted-service entry does not assert a license for every bundled processing dependency." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review of official documentation, repository and the direct tool publication on 2026-09-10. No analysis job, independent benchmark or developer verification was performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "analysis_types": [ "untargeted", "volatilomics" ], "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "feature_table", "spectra", "report" ], "other_output_formats": [ "Deconvolved GC-MS spectra and downloadable tables" ], "input_formats": [ "netcdf", "mzml", "mzxml" ] }, "publications": [ { "citation": "Aksenov AA, et al. Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data. Nature Biotechnology 39, 169–173 (2021).", "type": "primary", "doi": "10.1038/s41587-020-0700-3" } ], "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "GNPS2 GC-MS processing/networking workflow." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "description": "MSHub-GC separates overlapping GC-MS signals computationally. Its GNPS2 workflow produces deconvolved spectra, a quantification table and a cluster summary. These outputs support subsequent library searching and molecular networking in the GC-MS analysis ecosystem.", "common_uses": [ "Recover component spectra from overlapping GC-MS chromatographic signals.", "Prepare quantitative and spectral outputs for downstream annotation." ], "scope": { "designed_for": [ "Untargeted GC-MS processing, including volatile-metabolite studies." ], "not_designed_for": [ "LC-MS/MS feature processing or automatic confirmation of every compound identity." ], "requirements": [ "GC-MS files in CDF, mzML or mzXML format, with one format used throughout the input folder.", "The correct chromatographic time unit and a GNPS2 account." ], "considerations": [ "Inspect deconvolution quality and chromatographic separation before interpreting component spectra.", "The linked paper describes the wider processing-and-networking system; the current launch page is the deconvolution workflow.", "Do not assume support for every vendor-native file type; confirm conversion and input compatibility in the current documentation." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "msi-explorer", "name": "MSI-Explorer", "summary": "Explore ion images and region-of-interest spectra in a napari imaging-MS interface.", "description": "MSI-Explorer adds imaging-MS exploration to napari, including preprocessing, ion-image display, region selection and database-assisted annotation.", "links": { "primary": "https://github.com/MMV-Lab/MSI-Explorer", "repository": "https://github.com/MMV-Lab/MSI-Explorer", "documentation": "https://github.com/MMV-Lab/MSI-Explorer#readme" }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "desktop_gui", "python_package" ], "resource_types": [ "software_application" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "interactive_dashboard" ] }, "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "BSD-3-Clause verified in the official repository LICENSE." }, "data": { "input_types": [ "imaging_data" ], "input_formats": [ "imzml" ], "output_types": [ "spectra", "imaging_data" ], "other_output_formats": [ "Exported spectra as CSV and spectrum plots" ] }, "common_uses": [ "Inspect spatial ion distributions and compare selected regions." ], "scope": { "designed_for": [ "Interactive exploration of imaging-MS datasets." ], "not_designed_for": [ "Bulk LC-MS feature-table processing or definitive identification from mass alone." ], "requirements": [ "A local napari environment with the MSI-Explorer plugin and compatible imzML data." ], "considerations": [ "Record preprocessing settings; normalization and region selection affect interpretation.", "Database mass matches require further evidence for structural identification." ] }, "publications": [ { "doi": "10.1021/acs.analchem.5c01513", "type": "primary", "citation": "MSI-Explorer: Napari-Powered Tool for Mass Spectrometry Imaging Data Analysis. Analytical Chemistry (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Selected from the Enveda intake. Official documentation and primary-paper DOI/title/year checked on 2026-10-21. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "msiflow", "name": "msiFlow", "summary": "msiFlow provides Snakemake workflows for MSI preprocessing, segmentation, registration to immunofluorescence microscopy and region-based statistical or machine-learning analysis.", "links": { "primary": "https://github.com/Immunodynamics-Engel-Lab/msiflow", "repository": "https://github.com/Immunodynamics-Engel-Lab/msiflow", "documentation": "https://github.com/Immunodynamics-Engel-Lab/msiflow#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "preprocessing", "visualization_reporting", "multiomics_integration" ], "capabilities": [ "container_support", "multivariate_statistics" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics", "multiomics" ], "interfaces": [ "command_line", "workflow_language", "web_app" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "linux", "containers" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "imaging_data" ], "output_types": [ "imaging_data", "report" ], "other_input_formats": [ "Workflow-specific MSI data, microscopy images and region/configuration data" ], "other_output_formats": [ "Processed MSI features, registered/segmented images and analysis reports" ] }, "publications": [ { "doi": "10.1038/s41467-024-55306-7", "type": "primary", "citation": "msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis. Nature Communications (2025)." } ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-03-17", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "msiFlow provides Snakemake workflows for MSI preprocessing, segmentation, registration to immunofluorescence microscopy and region-based statistical or machine-learning analysis. Docker and local routes offer graphical or command-line execution.", "scope": { "designed_for": [ "msiFlow provides Snakemake workflows for MSI preprocessing, segmentation, registration to immunofluorescence microscopy and region-based statistical or machine-learning analysis." ], "not_designed_for": [ "Guaranteed molecular identification or accurate cross-modal registration without inspecting image alignment and experimental context." ], "requirements": [ "Docker or the documented local environment, workflow-compatible MSI/microscopy data and configuration; registration and region comparisons additionally require corresponding images and region/group information." ], "considerations": [ "Different workflows require different prepared inputs. Check preprocessing, registration and region definitions before interpreting spatial statistics or classifier results." ] }, "common_uses": [ "msiFlow provides Snakemake workflows for MSI preprocessing, segmentation, registration to immunofluorescence microscopy and region-based statistical or machine-learning analysis." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "msionization", "name": "MSIonization", "aliases": [], "summary": "MSIonization is a Python package for predicting the optimal ionization mode with higher ionization efficiency for organic molecules from their SMILES representations.", "links": { "primary": "https://github.com/sepehribakhtyar/MSIonization", "repository": "https://github.com/sepehribakhtyar/MSIonization" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "data_acquisition" ], "capabilities": [] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "python_package" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.6c01937", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-23" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/56", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "msireader", "name": "MSiReader", "summary": "Vendor-neutral desktop software for visualization, analysis and interpretation of high-resolution mass spectrometry imaging datasets.", "description": "MSiReader originated as an open academic MATLAB application and is now professionally maintained by MSI Software Solutions. Current Pro and BioPharma editions provide interactive MSI visualization and analysis for high-resolution accurate-mass imaging datasets.", "links": { "primary": "https://msireader.com/", "homepage": "https://msireader.com/", "documentation": "https://msireader.com/support" }, "resource_types": [ "software_application" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "interactive_dashboard" ], "secondary": [ "statistics" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "desktop_gui" ], "access": { "model": "freemium", "notes": "The current product retains limited freeware functionality after its trial. Pro and BioPharma features require paid licenses; check the current feature comparison. Historical academic releases used a different distribution model." }, "data": { "input_types": [ "imaging_data" ], "output_types": [ "imaging_data", "statistical_results" ], "input_formats": [ "imzml" ], "other_output_formats": [ "Heatmaps, spectra and m/z-abundance data (edition dependent)" ] }, "publications": [ { "citation": "Robichaud G, Garrard KP, Barry JA, Muddiman DC. MSiReader: an open-source interface to view and analyze high resolving power MS imaging files on Matlab platform. Journal of the American Society for Mass Spectrometry 24, 718–721 (2013).", "type": "primary", "doi": "10.1007/s13361-013-0607-z" } ], "common_uses": [ "Inspect and analyze vendor-neutral high-resolution MSI datasets.", "Visualize spatial ion distributions and compare regions." ], "scope": { "designed_for": [ "Interactive mass spectrometry imaging analysis." ], "not_designed_for": [ "Treating ion-image colocalization as conclusive structural identification." ], "requirements": [ "Imaging data supported by the selected edition and a compatible desktop installation.", "Check licensing and system requirements for the intended feature set." ], "considerations": [ "Current product capabilities, licensing and supported formats should be checked against the vendor documentation because the software has evolved substantially from the original academic release." ] }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Current vendor product, support and feature-comparison pages and original publication metadata reviewed on 2026-09-10. No installation or benchmark performed. The 2013 paper documents the original academic software, not the complete current commercial feature set." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_editorial_review": "2026-09-10" }, "related_tools": [ { "slug": "metaspace", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mslinear", "name": "MSlineaR", "aliases": [], "summary": "MSlineaR, is an open-source R-based software tool implementing a six-step process to assess dilution-dependent response behavior in metabolomic data sets.", "links": { "primary": "https://github.com/bih-metabolomics/MSlineaR", "repository": "https://github.com/bih-metabolomics/MSlineaR" }, "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "secondary": [], "capabilities": [] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "r_package" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.1021/acs.analchem.5c04480", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-23" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/48", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-23", "updated_at": "2026-09-23", "last_verified": "2026-09-23" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "msms-chooser", "name": "MSMS-Chooser", "summary": "A GNPS workflow for automatically selecting candidate reference MS/MS spectra from LC-MS data of known standards using supplied molecular structures and expected precursor masses.", "links": { "primary": "https://ccms-ucsd.github.io/GNPSDocumentation/msmschooser/", "documentation": "https://ccms-ucsd.github.io/GNPSDocumentation/msmschooser/" }, "resource_types": [ "web_service", "workflow_pipeline" ], "functions": { "primary": "spectral_analysis", "secondary": [ "annotation_identification" ], "capabilities": [ "reference_spectrum_selection", "ms2_processing" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "targeted", "metabolite_profiling" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "technical": { "operating_systems": [ "browser" ], "api_available": false }, "data": { "input_types": [ "spectra", "molecular_structures" ], "output_types": [ "spectra", "report" ], "input_formats": [ "mzml", "mzxml", "csv_tsv", "smiles_inchi" ], "output_formats": [ "csv_tsv" ] }, "publications": [], "common_uses": [ "Select MS/MS spectra corresponding to known standards from open-format data.", "Prepare candidate reference spectra for validation and library contribution.", "Link the selected spectrum back to its source standard and acquisition metadata before library submission." ], "scope": { "designed_for": [ "Extracting candidate reference MS/MS scans when structures or formulas of standards are known." ], "requirements": [ "Open-format mzML or mzXML data plus the MSMS-Chooser submission template." ], "considerations": [ "Automated selections should be visually reviewed and validated before use as reference spectra.", "Inspect precursor/adduct assignment and potential co-isolation or contamination before accepting a scan as a reference spectrum." ], "not_designed_for": [ "Untargeted identification of unknown compounds without prior standard information." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "targeted_msms", "flow_injection", "dda" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-21" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed. The official workflow documentation is the direct reference. No dedicated methods paper was identified in this pass; cite the GNPS platform and workflow details as applicable." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-21", "updated_at": "2026-09-06", "last_verified": "2026-08-21", "last_editorial_review": "2026-09-06" }, "aliases": [ "MS/MS-Chooser" ], "description": "MSMS-Chooser extracts candidate reference tandem mass spectra from measurements of known standards. A submission table connects files to molecular structures and expected precursor information; the GNPS workflow finds candidate scans for subsequent inspection. Its role is reference-spectrum preparation. The identity and purity of the starting material, precursor assignment and spectrum quality still require curator review before library deposition." }, { "$schema": "../../schemas/tool.schema.json", "slug": "msnbase", "name": "MSnbase", "summary": "Manipulate, process and visualize mass-spectrometry data within R.", "description": "MSnbase supplies data classes and functions spanning spectra and quantitative measurements. It is infrastructure used in analytical workflows, including metabolomics.", "links": { "primary": "https://bioconductor.org/packages/MSnbase/", "documentation": "https://bioconductor.org/packages/MSnbase/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "ms2_processing" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Open-source software; use the official installation, license and citation guidance for the selected release." }, "data": { "input_types": [ "spectra", "feature_table" ], "output_types": [ "spectra", "feature_table" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Spectra and MSnbase quantitative objects" ], "other_output_formats": [ "MSnbase spectral and quantitative objects" ] }, "publications": [ { "doi": "10.1093/bioinformatics/btr645", "citation": "Gatto L, Lilley KS. MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28, 288-289 (2012).", "type": "primary", "note": "Foundational MSnbase software paper; the original publication focuses on isobaric tagged mass spectrometry." } ], "resource_citations": [ { "citation": "MSnbase developers. MSnbase: Base Functions and Classes for Mass Spectrometry and Proteomics. Bioconductor package record.", "type": "software", "doi": "10.18129/B9.bioc.MSnbase" } ], "common_uses": [ "Inspect and manipulate spectra programmatically.", "Maintain compatibility with R workflows built around MSnbase objects." ], "scope": { "designed_for": [ "Inspect and manipulate spectra programmatically." ], "not_designed_for": [ "A ready-made end-to-end metabolomics pipeline without additional methods." ], "requirements": [ "R/Bioconductor and data compatible with the package import functions." ], "considerations": [ "Check object compatibility when combining older MSnbase workflows with newer Spectra-based packages." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "spectra", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "msnlib", "name": "MSnLib", "summary": "Open multistage fragmentation reference spectra and a reproducible library-generation workflow implemented with mzmine.", "description": "MSnLib combines reference-compound measurements, curated metadata and mzmine processing to generate spectral libraries organized as fragmentation trees. The 2025 publication describes more than two million spectra from 30,008 compounds; these are publication-version figures, not a live count.", "links": { "primary": "https://doi.org/10.1038/s41592-025-02813-0", "documentation": "https://github.com/merlin-ms/mass-spectral-library-network/tree/master/libraries/MSnLib", "repository": "https://github.com/merlin-ms/mass-spectral-library-network" }, "resource_types": [ "spectral_library", "workflow_pipeline" ], "functions": { "primary": "reference_data_search", "secondary": [ "spectral_analysis" ], "capabilities": [ "reference_spectrum_selection" ] }, "platforms": [ "direct_infusion_ms", "lc_ms" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "notes": "Generation uses open-source mzmine. MERLIN metadata and acquisition resources carry an MIT license; consult the selected library release for data reuse terms." }, "data": { "input_types": [ "spectra", "compound_list" ], "other_input_formats": [ "Reference measurements and curated compound metadata" ], "output_types": [ "spectral_library" ], "other_output_formats": [ "Release-specific spectral-library files and multistage fragmentation trees" ] }, "common_uses": [ "Use reference fragmentation spectra for annotation or method development.", "Generate libraries from measured reference compounds using mzmine." ], "scope": { "designed_for": [ "Reference spectral resources and reproducible library generation." ], "not_designed_for": [ "A standalone end-to-end statistical analysis of biological cohorts." ], "requirements": [ "Compatible library-search software for using released spectra; mzmine and suitable measurements for generation." ], "considerations": [ "Match polarity, fragmentation stage and acquisition conditions when interpreting spectral similarity.", "Confirm library-release formats and licensing before reuse." ] }, "publications": [ { "type": "primary", "doi": "10.1038/s41592-025-02813-0", "citation": "Brungs C, Schmid R, Heuckeroth S, et al. MSnLib: efficient generation of open multi-stage fragmentation mass spectral libraries. Nature Methods 22, 2028-2031 (2025)." } ], "related_tools": [ { "slug": "mzmine", "relationship": "integration", "note": "Published library-generation processing is implemented in mzmine." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Primary publication identity and MERLIN MSnLib documentation verified. Desktop interface refers to mzmine generation, not a separate MSnLib application. Downloads and generation were not independently executed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-28", "updated_at": "2026-09-28", "last_verified": "2026-09-28", "last_editorial_review": "2026-09-28" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "msnovelist", "name": "MSNovelist", "summary": "A SIRIUS-integrated de novo structure-generation method that proposes molecular structures from a predicted molecular formula and CSI:FingerID fingerprint when database search is insufficient.", "description": "MSNovelist generates candidate chemical structures de novo from a molecular formula and predicted molecular fingerprint derived from MS/MS data. The original standalone implementation and backend are end-of-life; since 2024, a retrained and expanded MSNovelist implementation has been integrated into SIRIUS 6 and is accessed through the SIRIUS webservice workflow. It is designed to propose structures beyond the contents of conventional structure databases.", "links": { "primary": "https://v6.docs.sirius-ms.io/", "documentation": "https://v6.docs.sirius-ms.io/methods-background/", "repository": "https://github.com/zamboni-lab/MSNovelist" }, "resource_types": [ "web_service" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation", "substructure_prediction", "molecular_formula_prediction" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "desktop_gui", "command_line", "api" ], "access": { "model": "free_academic", "notes": "The current MSNovelist implementation is integrated into SIRIUS 6 and requires SIRIUS webservice access. The linked standalone repository is archived and retained for historical reference." }, "publications": [ { "doi": "10.1038/s41592-022-01486-3", "citation": "Stravs MA, Dührkop K, Böcker S, et al. MSNovelist: de novo structure generation from mass spectra. Nat Methods 19, 865-870 (2022).", "type": "primary" } ], "common_uses": [ "Generate de novo molecular-structure candidates when database search cannot provide a satisfactory candidate.", "Explore plausible structures from SIRIUS molecular formulas and CSI:FingerID-predicted fingerprints.", "Prioritize candidate structures for unknown metabolites that are poorly represented in existing structure databases." ], "scope": { "designed_for": [ "De novo candidate generation from accurate-mass MS/MS data for small molecules." ], "requirements": [ "SIRIUS molecular-formula/fingerprint results and access to the SIRIUS webservice." ], "considerations": [ "Generated structures are hypotheses rather than definitive identifications and require substantial orthogonal validation.", "The historical standalone MSNovelist repository is archived; current users should use the implementation integrated into SIRIUS 6.", "A chemically valid generated candidate is a hypothesis; inspect alternative candidates and preserve the molecular formula and prediction settings." ], "not_designed_for": [ "Experimental confirmation of a generated structure or its stereochemistry." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "sirius", "relationship": "part_of" }, { "slug": "csi-fingerid", "relationship": "uses" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "api_available": true }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures", "report" ], "input_formats": [ "mzml", "mzxml", "mgf", "msp" ], "output_formats": [ "csv_tsv", "json" ], "other_input_formats": [ "SIRIUS project with formula and fingerprint predictions" ], "other_output_formats": [ "SIRIUS generated structure-candidate tables" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mspack", "name": "mspack", "summary": "mspack provides lossless and configurable lossy compression of mass-spectrometry arrays through a C++ API and example mzML/mzXML command-line implementation.", "links": { "primary": "https://github.com/fhanau/mspack", "repository": "https://github.com/fhanau/mspack", "documentation": "https://github.com/fhanau/mspack#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_conversion", "secondary": [ "data_management_sharing" ], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "command_line" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "C++" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra" ], "other_input_formats": [ "Supported mzML/mzXML files or arrays through the C++ API" ], "other_output_formats": [ "mgz/bsc compressed files and reconstructed spectral XML" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2021-09-16", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "mspack provides lossless and configurable lossy compression of mass-spectrometry arrays through a C++ API and example mzML/mzXML command-line implementation. It supports gzip and the optional bsc compression backend.", "scope": { "designed_for": [ "mspack provides lossless and configurable lossy compression of mass-spectrometry arrays through a C++ API and example mzML/mzXML command-line implementation." ], "not_designed_for": [ "Guaranteed compatibility with all mzML encodings or ion-mobility arrays, or numerically lossless results when lossy transforms are selected." ], "requirements": [ "C++11-compatible build tools and gzip, with bsc optional; example readers require supported XML files containing uncompressed raw mass/intensity arrays." ], "considerations": [ "Mass values must increase in the example implementation. XML whitespace is not preserved, and mzXML support is restricted to 32-bit data without block I/O." ] }, "common_uses": [ "mspack provides lossless and configurable lossy compression of mass-spectrometry arrays through a C++ API and example mzML/mzXML command-line implementation." ], "publications": [ { "doi": "10.1093/bioinformatics/btab636", "type": "primary", "citation": "mspack: efficient lossless and lossy mass spectrometry data compression. Bioinformatics (2021)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "msquality", "name": "MsQuality", "summary": "Calculate standardized per-sample quality metrics from mass spectrometry data in R.", "description": "MsQuality uses the mzQC controlled vocabulary to calculate low-level quality metrics from Spectra or MsExperiment objects. It supports tracking data quality across runs but is not itself a signal-drift correction algorithm.", "links": { "primary": "https://bioconductor.org/packages/MsQuality/", "repository": "https://github.com/tnaake/MsQuality", "documentation": "https://bioconductor.org/packages/MsQuality/" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "capabilities": [ "spectral_data_io" ] }, "access": { "model": "open_source", "license": "GPL-3.0", "notes": "License checked against official package metadata or repository. Local setup requires the documented dependencies; no installation or hosted-service availability test was performed." }, "data": { "input_types": [ "spectra" ], "output_types": [ "report" ], "other_input_formats": [ "Spectra or MsExperiment objects" ], "other_output_formats": [ "Per-sample quality-metric results from the R interface" ] }, "common_uses": [ "Calculate comparable low-level QC summaries for MS-metabolomics runs." ], "scope": { "designed_for": [ "Calculate standardized per-sample quality metrics from mass spectrometry data in R." ], "not_designed_for": [ "Confirming compound identity or replacing study design and scientific review." ], "requirements": [ "A compatible R/Bioconductor installation and populated spectral objects.", "Spectra or MsExperiment objects with retention time, m/z and intensity information" ], "considerations": [ "Available metrics depend on the information stored in each input; low-level metrics do not replace experimental QC design." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btad618", "type": "primary", "citation": "MsQuality: an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data. Bioinformatics (2023)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate verified against official documentation, source/package metadata and primary-publication DOI/title/year on 2026-10-02. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" }, "related_tools": [ { "slug": "spectra", "relationship": "integration", "note": "Metrics can be computed from Spectra objects." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "multibac", "name": "MultiBaC", "summary": "A Bioconductor R package for batch-effect correction in single-omics and multi-omics datasets, including metabolomics.", "links": { "primary": "https://bioconductor.org/packages/MultiBaC/", "homepage": "https://bioconductor.org/packages/MultiBaC/", "repository": "https://github.com/ConesaLab/MultiBaC", "documentation": "https://bioconductor.org/packages/release/bioc/html/MultiBaC.html" }, "resource_types": [ "programming_package" ], "functions": { "primary": "normalization", "secondary": [ "multiomics_integration" ], "capabilities": [ "batch_effect_correction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling", "multiomics" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Multi-omics R assay matrices and batch/experimental-design objects" ], "other_output_formats": [ "Corrected R multi-omics data and diagnostic/model objects" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2022-01-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "MultiBaC corrects batch effects in multi-omics datasets distributed across acquisition events or laboratories. It uses a shared omic layer to transfer correction information between batches and is implemented as an R/Bioconductor package.", "scope": { "designed_for": [ "A Bioconductor R package for batch-effect correction in single-omics and multi-omics datasets, including metabolomics." ], "not_designed_for": [ "Unconstrained correction of disjoint omics batches with no common layer, or raw MS preprocessing." ], "requirements": [ "R/Bioconductor dependencies and correctly organized multi-omics assay/design objects; at least one common omic data type must occur in every batch considered." ], "considerations": [ "Shared-layer quality and experimental design constrain correction. Verify that biological contrasts remain meaningful and do not treat corrected cross-omics association as causality." ] }, "common_uses": [ "A Bioconductor R package for batch-effect correction in single-omics and multi-omics datasets, including metabolomics." ], "publications": [ { "doi": "10.1093/bioinformatics/btac132", "type": "primary", "citation": "MultiBaC: an R package to remove batch effects in multi-omic experiments. Bioinformatics (2022)." }, { "doi": "10.1177/0962280220907365", "type": "primary", "citation": "MultiBaC: A strategy to remove batch effects between different omic data types. Statistical Methods in Medical Research (2020)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "multistep-massql", "name": "Multi-step MassQL", "summary": "Apply a predefined sequence of MassQL queries to GNPS2 networking spectra to investigate bile acids and their isomers.", "links": { "primary": "https://multistep-massql.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Multi-Step_MassQL/" }, "resource_types": [ "web_service" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "ms2_processing", "compound_class_prediction" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly hosted MetaboApp. GNPS2 infrastructure is open to the academic community; its documentation directs industry users to the maintainers or UCR Technology Transfer. Hosted access does not establish a software or reference-data license." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against the linked official app documentation on 2026-09-10. No standalone publication for this particular app was verified; the documentation citation identifies the reviewed implementation. Underlying method and application papers are not presented as dedicated app papers. No analysis job or independent benchmark was run; no developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list", "report" ], "other_input_formats": [ "Public GNPS2 CMN/FBMN task ID" ], "other_output_formats": [ "Interactive classification tree and result tables" ] }, "scope": { "designed_for": [ "Bile-acid-focused analysis of completed molecular-networking results." ], "not_designed_for": [ "A general-purpose sequential-query interface for arbitrary metabolite classes.", "Unqualified confirmation of stereochemistry from every matching spectrum." ], "requirements": [ "A completed, public GNPS2 CMN or FBMN task containing relevant MS/MS spectra." ], "considerations": [ "Current documentation limits the app to bile acids; broader classes are described as future work.", "Mixed spectra can satisfy multiple classifications. Inspect the evidence behind each assignment.", "The app uses preconfigured queries; document the application version and upstream task when reporting results." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Standalone MetaboApp that can be launched directly or from a GNPS2 task." }, { "slug": "gnps", "relationship": "integration", "note": "Consumes GNPS2 tasks or ecosystem data." }, { "slug": "massql", "relationship": "integration", "note": "Uses the MassQL query language." }, { "slug": "gnps-classical-molecular-networking", "relationship": "integration", "note": "Accepts a completed public CMN task." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Accepts a completed public FBMN task." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "publications": [], "resource_citations": [ { "citation": "GNPS2 Documentation. Multi-step MassQL. Accessed 10 September 2026.", "type": "documentation", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_Multi-Step_MassQL/" } ], "description": "Multi-step MassQL applies successive fragmentation rules to spectra from a GNPS2 networking task. The documented app is configured for bile-acid classification, with a classification tree and result tables for inspecting individual features.", "common_uses": [ "Apply consistent bile-acid query logic across processed datasets.", "Inspect alternative classifications for a feature." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mummichog", "name": "mummichog", "summary": "Generate pathway and network hypotheses from untargeted metabolomics feature tables.", "description": "mummichog tests patterns of candidate metabolite matches across metabolic networks, allowing functional exploration before every feature has an identification.", "links": { "primary": "https://github.com/metabolomics-cloud/mummichog", "documentation": "https://github.com/metabolomics-cloud/mummichog", "repository": "https://github.com/metabolomics-cloud/mummichog" }, "resource_types": [ "software_application" ], "functions": { "primary": "pathway_interpretation", "capabilities": [ "pathway_enrichment" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "command_line" ], "access": { "model": "source_available", "notes": "Public source code; pin the intended release branch and consult its license." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Feature table with m/z and statistical results" ], "other_output_formats": [ "Pathway enrichment and network results" ] }, "publications": [ { "citation": "Li S, Park Y, Duraisingham S, et al. Predicting network activity from high throughput metabolomics. PLoS Comput Biol 9, e1003123 (2013).", "type": "primary", "doi": "10.1371/journal.pcbi.1003123" } ], "common_uses": [ "Prioritize pathways associated with differential features.", "Explore a feature table with a species-appropriate metabolic model." ], "scope": { "designed_for": [ "Prioritize pathways associated with differential features." ], "not_designed_for": [ "Confirming individual metabolite identities or measuring metabolic flux." ], "requirements": [ "Feature masses, statistical results, acquisition information and a suitable metabolic model." ], "considerations": [ "Background features and adduct assumptions affect enrichment. The current repository labels version 3 as development and retains a version 2 branch." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metaboanalyst", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "mwformer", "name": "MWFormer", "summary": "Estimate molecular weight from electron-ionization spectra.", "description": "MWFormer uses a transformer model to estimate molecular weight from EI peak patterns, providing an additional constraint for candidate searches.", "links": { "primary": "https://github.com/zhanghailiangcsu/MWFormer#readme", "repository": "https://github.com/zhanghailiangcsu/MWFormer", "documentation": "https://github.com/zhanghailiangcsu/MWFormer#readme" }, "platforms": [ "gc_ms" ], "interfaces": [ "command_line", "web_app" ], "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "capabilities": [ "spectral_data_io", "interactive_dashboard" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "MIT license verified in the official source repository. Training datasets and model downloads may have separate terms. No execution or independent performance benchmark." }, "data": { "input_types": [ "spectra" ], "output_types": [ "report" ], "other_input_formats": [ "EI spectra in MSP or entered peak lists" ], "other_output_formats": [ "Predicted molecular weights; batch spreadsheet output" ] }, "common_uses": [ "Estimate molecular weight from electron-ionization spectra." ], "scope": { "designed_for": [ "MWFormer uses a transformer model to estimate molecular weight from EI peak patterns, providing an additional constraint for candidate searches." ], "not_designed_for": [ "Molecular formula or complete structure identification, or LC-ESI-MS/MS prediction." ], "requirements": [ "Python, PyTorch, RDKit and pretrained weights; local Streamlit interface or adapted example scripts." ], "considerations": [ "Predicted weights are uncertain model estimates rather than measured molecular ions.", "Examples contain local paths that may need adaptation; retraining requires appropriately licensed spectral data." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c03781", "type": "primary", "citation": "MWFormer: Estimation of Molecular Weights from Electron Ionization Mass Spectra for Improved Library Searching. Analytical Chemistry (2025; online 2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mza", "name": "MZA", "summary": "Convert multidimensional mass-spectrometry data from vendor formats or mzML into an HDF5-based MZA structure for efficient storage and programmatic access.", "description": "MZA is a stand-alone converter and data structure for multidimensional mass-spectrometry data, including LC-IM-MS and DIA datasets. It converts supported proprietary files and mzML into an HDF5-based representation designed to make large, heterogeneous MS datasets easier to access from multiple programming languages and operating systems.", "links": { "primary": "https://github.com/PNNL-m-q/mza", "repository": "https://github.com/PNNL-m-q/mza", "documentation": "https://github.com/PNNL-m-q/mza" }, "resource_types": [ "software_application" ], "functions": { "primary": "data_conversion", "secondary": [ "data_management_sharing" ], "capabilities": [ "ms2_processing" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "command_line" ], "access": { "model": "source_available", "notes": "The repository provides source code and examples, while binary use can also depend on vendor data-access libraries and their terms." }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "raw_instrument_data" ], "input_formats": [ "vendor_raw", "mzml" ], "output_formats": [ "hdf5" ], "other_output_formats": [ "MZA" ] }, "publications": [ { "doi": "10.1021/acs.jproteome.2c00313", "citation": "Bilbao A, et al. MZA: a data conversion tool to facilitate software development and artificial intelligence research in multidimensional mass spectrometry. J Proteome Res (2023).", "type": "primary" } ], "common_uses": [ "Convert LC-MS, LC-IM-MS and DIA datasets into a common HDF5-based representation.", "Make multidimensional MS data accessible to Python, R and other HDF5-capable environments." ], "scope": { "designed_for": [ "Data conversion and storage for large multidimensional MS datasets." ], "not_designed_for": [ "Feature detection, compound annotation or statistical interpretation by itself." ], "requirements": [ "Supported vendor-format data or mzML input and the dependencies required by the selected converter." ], "considerations": [ "Vendor-format conversion may depend on vendor-provided libraries and licensing terms." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Added from the 2026 computational-metabolomics review population and Springer methods coverage; functionality checked against the official PNNL repository on 10 September 2026." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dia" ], "ion_mobility": "supported" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mzapy", "name": "mzapy", "summary": "Read, extract, calibrate, and visualize multidimensional mass-spectrometry data in Python, including LC-IM-MS datasets stored in MZA and selected vendor-supported formats.", "description": "mzapy is a Python package for accessing and manipulating multidimensional mass-spectrometry data. It was developed around MZA and supports data extraction, plotting, selected calibration tasks, signal processing, peak finding, and programmatic access to dimensions such as retention time, ion mobility, mass-to-charge ratio, and intensity. It is particularly useful for scripting LC-IM-MS workflows and for prototyping downstream analyses on data converted to MZA.", "links": { "primary": "https://github.com/PNNL-m-q/mzapy", "repository": "https://github.com/PNNL-m-q/mzapy", "documentation": "https://mzapy.readthedocs.io/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [ "data_conversion", "visualization_reporting" ], "capabilities": [ "chromatogram_building", "peak_detection", "ms2_processing" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "mzapy is distributed as open-source Python software; current installation and dependency details are documented in the project documentation." }, "technical": { "programming_languages": [ "Python" ], "package_managers": [ "pip" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "spectra", "feature_table" ], "input_formats": [ "hdf5", "other" ], "output_formats": [ "csv_tsv", "other" ], "other_input_formats": [ "MZA" ] }, "common_uses": [ "Extract retention-time, ion-mobility and m/z slices from multidimensional MS datasets.", "Build scripted LC-IM-MS analysis and visualization workflows in Python.", "Work programmatically with MZA files generated from vendor or mzML data." ], "publications": [ { "doi": "10.1021/acs.analchem.3c01653", "citation": "Ross DH, Bilbao A, Lee JY, Zheng X. mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format. Anal Chem. 2023;95:9428-9431.", "type": "primary" } ], "scope": { "designed_for": [ "Programmatic access and lightweight analysis of multidimensional MS data, especially ion-mobility datasets." ], "not_designed_for": [ "Complete untargeted feature processing or metabolite identification by itself." ], "requirements": [ "A compatible Python environment and supported input data such as MZA." ], "considerations": [ "Ion-mobility workflows require appropriate calibration and interpretation of the mobility dimension; extraction utilities do not by themselves establish metabolite identity." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Added from the 2026 computational-metabolomics review population and Springer methods coverage; functionality checked against the official PNNL repository, documentation and primary publication on 10 September 2026." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia" ], "ion_mobility": "supported" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mzmine", "name": "MZmine", "summary": "A modular mass-spectrometry environment for raw-data import, feature detection/alignment, multimodal LC/IMS/GC/MSI processing, molecular networking, annotation, lipid analysis, visualization, normalization, statistics, and downstream exports.", "description": "MZmine is a modular mass-spectrometry processing environment with graphical, batch, wizard-guided, and command-line workflows. Current MZmine supports LC-MS/MS DDA and DIA, LC-IMS-MS, GC-EI/CI-MS, direct-infusion and imaging workflows; feature detection, resolving, alignment and filtering; ion/isotope/adduct grouping; spectral-library and lipid annotation; interactive molecular networking using classical/modified cosine and learned spectral-similarity models; statistics and fragmentation dashboards; and direct exports/integrations with GNPS, SIRIUS, MetaboAnalyst, and other downstream resources.", "links": { "primary": "https://mzmine.github.io/", "homepage": "https://mzmine.github.io/", "repository": "https://github.com/mzmine/mzmine", "documentation": "https://mzmine.github.io/mzmine_documentation/", "issue_tracker": "https://github.com/mzmine/mzmine/issues" }, "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "secondary": [ "annotation_identification", "molecular_networking", "normalization", "quality_control", "spectral_analysis", "statistics", "visualization_reporting" ], "capabilities": [ "adduct_isotope_grouping", "batch_effect_correction", "blank_filtering", "chromatogram_building", "gap_filling", "interactive_dashboard", "ion_identity_networking", "library_matching", "ms2_processing", "multivariate_statistics", "network_visualization", "peak_detection", "retention_time_alignment", "spectral_deconvolution", "structure_database_search", "univariate_statistics" ] }, "platforms": [ "lc_ms", "direct_infusion_ms", "maldi_ms", "imaging_ms", "gc_ms" ], "analysis_types": [ "lipidomics", "metabolite_profiling", "spatial_metabolomics", "targeted", "untargeted", "volatilomics" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "free_academic", "notes": "The current MZmine site provides the Community edition free of charge through an academic program for eligible university users and also offers commercial/enterprise packages. Source code remains publicly available on GitHub; users should verify current licensing terms for their use case.", "license": "MIT" }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "programming_languages": [ "Java" ], "api_available": false }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "feature_table", "spectra", "compound_list", "report" ], "input_formats": [ "vendor_raw", "mzml", "mzxml", "netcdf", "imzml" ], "output_formats": [ "csv_tsv", "mgf", "msp", "mztab_m" ] }, "publications": [ { "doi": "10.1038/s41587-023-01690-2", "citation": "Schmid R, Heuckeroth S, Korf A, et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol 41, 447-449 (2023).", "type": "primary" }, { "doi": "10.1038/s41596-024-00996-y", "citation": "Heuckeroth S, Damiani T, Smirnov A, et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nat Protoc 19, 2597-2641 (2024).", "type": "methods" }, { "doi": "10.1021/acs.analchem.8b05493", "citation": "Korf A, Jeck V, Schmid R, et al. Lipid Species Annotation at Double Bond Position Level with Custom Databases by Extension of the MZmine 2 Open-Source Software Package. Anal Chem 91, 5098-5105 (2019).", "type": "methods", "note": "Introduces a lipid-annotation extension implemented within MZmine 2." } ], "common_uses": [ "Process untargeted LC-MS/MS DDA/DIA and LC-IMS-MS data from raw files through aligned feature tables and annotations.", "Perform GC-EI/CI-MS spectral deconvolution, MS-imaging processing, direct-infusion analyses, and specialized lipidomics workflows.", "Run interactive molecular/ion-identity networking locally using spectral similarity and machine-learning similarity models.", "Perform spectral-library and rule-based lipid annotation, interactive statistics/fragmentation review, and exports to GNPS, SIRIUS, and downstream analysis tools.", "Generate and curate reference spectral-library data using batch/wizard-assisted processing.", "Inspect aligned features and associated spectra before exporting to GNPS, SIRIUS or statistical analysis." ], "related_tools": [ { "slug": "gnps", "relationship": "integration" }, { "slug": "sirius", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24", "latest_version": "4.10.6", "latest_release_date": "2026-06-03" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "Modular processing and inspection of untargeted LC-MS and LC-IMS-MS datasets, including feature-based workflows that retain links to MS/MS and ion-mobility information.", "GC-EI-MS spectral deconvolution and alignment, MS-imaging workflows, and interactive or batch processing with exports to downstream annotation and molecular-networking resources." ], "not_designed_for": [ "A replacement for every downstream structure-elucidation or statistical-analysis platform; specialist tools are commonly used after preprocessing." ], "requirements": [ "Desktop installation; stable releases provide Windows, macOS, and Linux packages and include the required Java runtime." ], "considerations": [ "Parameter choices for feature detection, resolving, alignment, normalization, networking, and filtering remain experiment dependent and should be preserved in batch files or wizard configurations.", "The 2024 Nature Protocols procedures were published for MZmine 3; current MZmine 4 workflows preserve core concepts but interfaces, licensing, and available modules continue to evolve, so current documentation should be consulted.", "MZmine Community is available through an academic program, while current project pages also advertise enterprise/commercial packages; users should verify current licensing for their setting.", "Retain the raw-data links and batch configuration with feature-table and MS/MS exports; downstream tools need matching feature identifiers." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia" ], "ion_mobility": "supported" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "mzquality", "name": "mzQuality", "summary": "An R package for metabolomics study quality control, including outlier detection, pooled-QC batch correction, reliability filtering, diagnostic visualization, and reporting.", "links": { "primary": "https://github.com/hankemeierlab/mzQuality", "repository": "https://github.com/hankemeierlab/mzQuality", "documentation": "https://hankemeierlab.github.io/mzQuality/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "secondary": [ "normalization", "visualization_reporting", "quantification" ], "capabilities": [ "batch_effect_correction", "qc_drift_correction", "calibration_curve" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 R package. The companion mzQualityDashboard is a separately installed interactive front end; it is not part of every package-only analysis." }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "report", "statistical_results" ], "input_formats": [ "csv_tsv" ], "other_output_formats": [ "SummarizedExperiment with metrics, selection flags and plot objects" ] }, "publications": [ { "doi": "10.1021/jasms.5c00073", "type": "primary", "citation": "mzQuality: An Open-Source Software Tool for Quality Monitoring and Reporting of Targeted Mass Spectrometry Measurements. Journal of the American Society for Mass Spectrometry (2025)." } ], "common_uses": [ "Flag unreliable compounds and samples before downstream analysis.", "Apply pooled-study-QC-based batch correction and inspect batch behavior.", "Generate QC plots and reporting outputs for processed metabolomics tables." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-08-11", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Quality assessment and filtering of processed metabolomics studies represented as tabular abundance data." ], "considerations": [ "Thresholds and reference-sample design determine which metrics are meaningful; flagged observations need experimental context before exclusion." ], "not_designed_for": [ "Raw spectral feature extraction or correction of study designs lacking the reference samples required by a selected metric." ], "requirements": [ "R/Bioconductor dependencies and correctly structured tabular data or a SummarizedExperiment; pooled study-QC samples are needed for QC-based correction and calibrants for concentration calculations." ] }, "description": "mzQuality evaluates processed quantitative MS studies using compound abundances, sample annotations and QC information. It computes QC metrics, supports pooled-QC batch correction and calibration-based quantification, and flags compounds or samples for inspection." }, { "$schema": "../../schemas/tool.schema.json", "slug": "natural-products-atlas", "name": "Natural Products Atlas", "summary": "Explore structures and literature for bacterial and fungal natural products.", "description": "The Natural Products Atlas links microbial natural products to origins and references, with tools for structural and chemical-space exploration.", "links": { "primary": "https://www.npatlas.org/", "documentation": "https://www.npatlas.org/" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Database content is CC BY-NC 4.0; consult terms for commercial reuse." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Names, molecular properties or structures" ], "other_output_formats": [ "Structures, origin records and references" ] }, "publications": [ { "citation": "Poynton EF, van Santen JA, Pin M, et al. The Natural Products Atlas 3.0: extending the database of microbially derived natural products. Nucleic Acids Res 53, D691-D699 (2025).", "type": "primary", "doi": "10.1093/nar/gkae1093" } ], "common_uses": [ "Find related microbial natural-product scaffolds.", "Trace a reported compound back to its isolation literature." ], "scope": { "designed_for": [ "Find related microbial natural-product scaffolds." ], "not_designed_for": [ "A complete reference for all plant, animal or synthetic compounds." ], "requirements": [ "A compound name, molecular property or structure-based search." ], "considerations": [ "Reported origin is contextual evidence; it does not establish that a compound in a new sample came from the same organism." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "lotus", "relationship": "complementary" }, { "slug": "coconut", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "neatms", "name": "NeatMS", "summary": "An open-source Python package that uses neural-network classification to label and filter false-positive MS1 peaks from untargeted LC-MS feature-detection workflows.", "links": { "primary": "https://github.com/bihealth/NeatMS", "repository": "https://github.com/bihealth/NeatMS", "documentation": "https://neatms.readthedocs.io/en/latest/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "secondary": [ "machine_learning" ], "capabilities": [ "feature_selection" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT code; the official repository is archived. Its documentation still says publication pending, while the subsequently published primary paper is cited here." }, "technical": { "operating_systems": [ "linux", "macos", "windows" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "feature_table", "raw_instrument_data" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Feature-detection results and chromatographic data via package objects" ], "other_output_formats": [ "Python feature objects with predicted peak-quality labels for filtering" ] }, "publications": [ { "doi": "10.1021/acs.analchem.1c02220", "type": "primary", "citation": "Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics. Analytical Chemistry (2022)." } ], "common_uses": [ "Remove low-quality or false-positive LC-MS features after peak detection.", "Apply pretrained or user-trained neural-network classifiers to chromatographic signals." ], "maintenance": { "status": "archived", "repository_last_activity": "2021-08-19", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Automated LC-MS signal-quality classification and filtering after feature detection." ], "considerations": [ "Check compatibility with the older environment and upstream export format. Classifier suitability depends on peak shapes and acquisition conditions." ], "not_designed_for": [ "Initial feature extraction, molecular annotation or a guarantee that every rejected low-quality signal is chemically irrelevant." ], "requirements": [ "A compatible Python environment, detected feature information, corresponding raw LC-MS data and an appropriate supplied or trained classification model; follow the package examples for the expected import structure." ] }, "description": "NeatMS labels and filters detected LC-MS MS1 signals using a neural-network classifier of peak quality. It is intended to follow upstream feature detection and uses raw chromatographic signal information together with the reported features." }, { "$schema": "../../schemas/tool.schema.json", "slug": "netid", "name": "NetID", "summary": "An R-based global network-optimization method for annotating untargeted LC-MS features using measured masses, retention times, known chemical relationships, and optional MS/MS evidence.", "links": { "primary": "https://github.com/LiChenPU/NetID", "repository": "https://github.com/LiChenPU/NetID", "documentation": "https://github.com/LiChenPU/NetID#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "molecular_networking" ], "capabilities": [ "adduct_isotope_grouping", "library_matching", "structure_database_search" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution with example inputs and workflow guidance. User-supplied libraries and reference data have their own usage terms." }, "technical": { "operating_systems": [ "windows", "macos", "linux" ], "programming_languages": [ "R", "C" ] }, "data": { "input_types": [ "raw_instrument_data", "feature_table", "spectra", "spectral_library" ], "output_types": [ "compound_list", "feature_table" ], "input_formats": [ "mzxml", "csv_tsv", "mgf" ], "other_output_formats": [ "R annotation/network objects and feature assignments for downstream inspection" ] }, "publications": [ { "doi": "10.1038/s41592-021-01303-3", "type": "primary", "citation": "Metabolite discovery through global annotation of untargeted metabolomics data. Nature Methods (2021)." } ], "common_uses": [ "Improve annotation coverage by optimizing assignments across a connected LC-MS feature network.", "Use adduct, isotope, fragmentation, and biochemical-transformation relationships to constrain candidate assignments." ], "maintenance": { "status": "unclear", "repository_last_activity": "2023-08-28", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Global annotation of untargeted LC-MS feature tables using network constraints." ], "requirements": [ "R environment, mzXML data, CSV peak and transformation tables, and an RDS reference-compound library; optional MS/MS matching also needs MGF spectra and an RDS spectral library." ], "considerations": [ "Candidate coverage and transformation rules constrain the network. Incorrect seeds or feature relationships can influence multiple assignments." ], "not_designed_for": [ "Unconstrained de novo structure elucidation or experimental confirmation of every network-propagated annotation." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "unspecified" ], "ion_mobility": "not_required" }, "description": "NetID globally annotates LC-MS features using a network of mass differences representing isotopes, adducts, fragments and biochemical transformations. Reference compounds and optional retention-time or tandem-spectral evidence constrain the optimization." }, { "$schema": "../../schemas/tool.schema.json", "slug": "nextflow4ms-dial", "name": "Nextflow4MS-DIAL", "summary": "A reproducible Nextflow workflow for LC-HRMS metabolomics that executes MS-DIAL-based processing across local, containerized, and high-performance computing environments.", "links": { "primary": "https://github.com/Nextflow4Metabolomics/nextflow4ms-dial", "repository": "https://github.com/Nextflow4Metabolomics/nextflow4ms-dial", "documentation": "https://github.com/Nextflow4Metabolomics/nextflow4ms-dial#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "preprocessing", "annotation_identification" ], "capabilities": [ "provenance_tracking", "container_support", "peak_detection", "retention_time_alignment", "library_matching" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "workflow_language", "command_line" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT workflow code; bundled or invoked programs and reference libraries retain their own terms. Use the documented execution profile and record the configuration." }, "technical": { "operating_systems": [ "linux", "macos", "containers" ], "programming_languages": [ "Nextflow" ], "containers": [ "Docker", "Singularity" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra", "spectral_library" ], "output_types": [ "feature_table", "compound_list", "report" ], "input_formats": [ "mzml", "other" ], "other_input_formats": [ "ABF" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1021/jasms.4c00364", "type": "primary", "citation": "Nextflow4MS-DIAL: A Reproducible Nextflow-Based Workflow for Liquid Chromatography–Mass Spectrometry Metabolomics Data Processing. Journal of the American Society for Mass Spectrometry (2025)." } ], "common_uses": [ "Run MS-DIAL preprocessing reproducibly on local systems or HPC.", "Preserve workflow configuration, execution logs, and containerized dependencies around an MS-DIAL analysis." ], "related_tools": [ { "slug": "ms-dial", "relationship": "dependency" } ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-05-30", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Reproducible execution of MS-DIAL LC-HRMS workflows across local, container, and HPC environments." ], "requirements": [ "Nextflow, Java and Docker or Singularity in the documented Linux/macOS setup, mzML/ABF data, MS-DIAL/MS-FLO configuration files and appropriately named reference libraries." ], "not_designed_for": [ "A new standalone feature-detection algorithm or conversion of every proprietary raw format without external converters." ], "considerations": [ "Results depend on MS-DIAL parameters and library compatibility. CPU/resource allocation and file naming must follow the workflow guidance on the chosen platform." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "unspecified" ], "ion_mobility": "unspecified" }, "description": "Nextflow4MS-DIAL orchestrates MS-DIAL and MS-FLO processing with Nextflow for reproducible LC-HRMS analyses. Container profiles support local and HPC execution and produce feature/annotation tables together with workflow reports." }, { "$schema": "../../schemas/tool.schema.json", "slug": "noreva", "name": "NOREVA", "summary": "Normalize metabolomics data and compare the performance of alternative normalization strategies.", "description": "NOREVA evaluates normalization using multiple criteria. Version 2 extends the approach to time-course and multi-class study designs.", "links": { "primary": "https://idrblab.org/noreva/", "documentation": "https://idrblab.org/noreva2020/NOREVA-Manual.pdf" }, "resource_types": [ "software_application" ], "functions": { "primary": "normalization", "capabilities": [ "batch_effect_correction" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "web_app", "r_package" ], "access": { "model": "free_web_service", "notes": "Web service and standalone R scripts are described in the official manual; review distribution terms." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Abundance matrices and study-design metadata" ], "other_output_formats": [ "Normalized matrices and evaluation results" ] }, "publications": [ { "citation": "Yang Q, Wang Y, Zhang Y, et al. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res 48, W436-W448 (2020).", "type": "primary", "doi": "10.1093/nar/gkaa258" } ], "common_uses": [ "Compare normalization choices before differential analysis.", "Assess the sensitivity of results to preprocessing strategy." ], "scope": { "designed_for": [ "Compare normalization choices before differential analysis." ], "not_designed_for": [ "Selecting a universally optimal normalization solely from class separation." ], "requirements": [ "A formatted abundance matrix, sample-group information and any reference or QC data required by the chosen evaluation." ], "considerations": [ "Avoid selecting methods using held-out outcome labels; assess biological plausibility and analytical QC alongside evaluation scores." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "stattarget", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "norm-iswsvr", "name": "Norm-ISWSVR", "summary": "Norm-ISWSVR normalizes metabolomics tables by combining internal-standard correction with support-vector-regression-based adjustment.", "links": { "primary": "https://github.com/Dingxian666/Norm-ISWSVR", "repository": "https://github.com/Dingxian666/Norm-ISWSVR", "documentation": "https://github.com/Dingxian666/Norm-ISWSVR#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "normalization", "secondary": [ "machine_learning" ], "capabilities": [ "batch_effect_correction", "qc_drift_correction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "python_package" ], "access": { "model": "free_academic", "notes": "README states a patent application and strictly prohibits commercial use. No unrestricted open-source grant is claimed." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Abundance CSV and sample CSV with optional standard/run metadata" ], "other_output_formats": [ "Normalized abundance tables and validation/tuning results" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2023-04-05", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "Norm-ISWSVR normalizes metabolomics tables by combining internal-standard correction with support-vector-regression-based adjustment. Its Python implementation supports matched or unmatched internal-standard settings, cross-validation and hyperparameter search.", "scope": { "designed_for": [ "Norm-ISWSVR normalizes metabolomics tables by combining internal-standard correction with support-vector-regression-based adjustment." ], "not_designed_for": [ "Correcting every biological/technical confounding pattern or establishing accurate absolute concentrations solely by normalization." ], "requirements": [ "Documented Python dependencies, abundance and sample CSV tables, internal-standard information and appropriate QC/run metadata for the selected mode." ], "considerations": [ "Review matched-standard assignments, QC coverage and biological preservation. Injection-order and batch columns are optional in the template but their absence limits corresponding diagnostics and interpretation." ] }, "common_uses": [ "Norm-ISWSVR normalizes metabolomics tables by combining internal-standard correction with support-vector-regression-based adjustment." ], "publications": [ { "doi": "10.1021/acs.analchem.1c05502", "type": "primary", "citation": "Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics. Analytical Chemistry (2022)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "np3-ms-workflow", "name": "NP3 MS Workflow", "summary": "Process LC-MS/MS data for natural-product profiling and molecular-network exploration.", "description": "NP3 MS Workflow combines preprocessing, spectral clustering, quantification, library annotation and molecular-network generation. Sample bioactivity metadata can support prioritization.", "links": { "primary": "https://github.com/danielatrivella/NP3_MS_Workflow", "repository": "https://github.com/danielatrivella/NP3_MS_Workflow", "documentation": "https://crisfbazz.github.io/NP3_MS_Workflow_Nextflow/" }, "platforms": [ "lc_ms" ], "interfaces": [ "command_line", "web_app" ], "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "preprocessing", "capabilities": [ "ms2_processing", "library_matching", "network_visualization" ] }, "access": { "model": "open_source", "notes": "License checked against official repository/package metadata. Installation and execution were not tested.", "license": "GPL-3.0-or-later" }, "data": { "input_types": [ "spectra", "sample_metadata" ], "output_types": [ "feature_table", "spectra", "report" ], "other_input_formats": [ "LC-MS/MS mzML or mzXML files and sample metadata" ], "other_output_formats": [ "MGF spectra, CSV count/peak-area tables and network/report files" ] }, "common_uses": [ "Process LC-MS/MS data for natural-product profiling and molecular-network exploration." ], "scope": { "designed_for": [ "NP3 MS Workflow combines preprocessing, spectral clustering, quantification, library annotation and molecular-network generation. Sample bioactivity metadata can support prioritization." ], "not_designed_for": [ "Demonstrating biological activity or establishing a structure solely from a network or library match." ], "requirements": [ "Compatible LC-MS/MS acquisition and prepared sample metadata.", "Local dependencies for the full CLI; a GNPS2 interface exposes a subset of the workflow." ], "considerations": [ "Bioactivity prioritization requires measured bioactivity metadata.", "Check ion-mode support against the selected version: the manual cautions that some negative-mode steps are less tested." ] }, "publications": [ { "doi": "10.1021/acs.analchem.3c05829", "type": "primary", "citation": "NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" }, "analysis_types": [ "untargeted" ], "aliases": [ "NP3", "NP³ MS Workflow" ], "related_tools": [ { "slug": "gnps", "relationship": "integration", "note": "The official documentation describes a GNPS2-hosted workflow interface and GNPS library searching; the full CLI has additional options." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "npclassifier", "name": "NPClassifier", "summary": "Deep neural-network tool that classifies natural-product structures into biosynthetic pathways, superclasses and structural classes.", "links": { "primary": "https://npclassifier.gnps2.org/", "documentation": "https://github.com/mwang87/NP-Classifier", "repository": "https://github.com/mwang87/NP-Classifier" }, "resource_types": [ "web_service", "api_service" ], "functions": { "primary": "annotation_identification", "capabilities": [ "compound_class_prediction" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app", "api" ], "access": { "model": "open_source", "license": "MIT", "notes": "The hosted classifier is publicly accessible. The repository specifies MIT for software and CC0 for its data, models and ontology. Local deployment is documented; the repository also states that queried structures are logged by the hosted service." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Editorial review of official documentation, repository and the direct tool publication on 2026-09-10. No analysis job, independent benchmark or developer verification was performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "compound_list" ], "input_formats": [ "smiles_inchi" ], "output_formats": [ "json" ], "other_input_formats": [ "SMILES strings (InChI input is not asserted)" ] }, "publications": [ { "citation": "Kim HW, Wang M, Leber CA, et al. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. Journal of Natural Products 84, 2795–2807 (2021).", "type": "primary", "doi": "10.1021/acs.jnatprod.1c00399", "pmid": "34662515" } ], "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Standalone classifier hosted within the GNPS2 ecosystem." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "description": "NPClassifier assigns natural-product pathway, superclass and class labels from a supplied chemical structure. Within MS metabolomics it can organize already annotated compounds or candidate structures. It does not infer a structure directly from a mass spectrum.", "common_uses": [ "Group candidate or annotated metabolites by natural-product chemical class.", "Add structure-derived class labels to downstream metabolomics summaries." ], "scope": { "designed_for": [ "Biosynthetically informed classification of supplied natural-product structures." ], "not_designed_for": [ "Annotating an unknown MS/MS spectrum without a candidate structure.", "Proving that a predicted class establishes biological origin or a biosynthetic route." ], "requirements": [ "A valid molecular structure supplied as SMILES.", "For local hosting, the model files and deployment dependencies described in the repository." ], "considerations": [ "Classification depends on the supplied structure; an incorrect structural annotation carries through to its class labels.", "The natural-product ontology has its own class definitions and coverage; preserve that context when comparing classifications." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "nplinker", "name": "NPLinker", "summary": "Ranks candidate links between microbial biosynthetic gene clusters and MS metabolomics spectra or molecular families using paired strain data.", "links": { "primary": "https://github.com/NPLinker/nplinker", "documentation": "https://nplinker.github.io/nplinker/latest/", "repository": "https://github.com/NPLinker/nplinker" }, "resource_types": [ "programming_package", "software_application" ], "functions": { "primary": "multiomics_integration", "secondary": [ "molecular_networking", "visualization_reporting" ], "capabilities": [ "metadata_contextualization" ] }, "platforms": [ "lc_ms", "platform_independent" ], "interfaces": [ "python_package", "web_app" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Open-source Python framework with a documented webapp interface. Current documentation requires Python 3.11 or later on Linux, macOS or Windows with WSL; external dependencies and reference databases require additional installation. Pin a release and reference-data versions." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official documentation, repository or archived release metadata and direct resource publications reviewed on 2026-09-12. Included for MS-based natural-product research. No installation, biological validation or independent benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "analysis_types": [ "multiomics", "untargeted" ], "data": { "input_types": [ "spectra", "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Supported GNPS network archives", "antiSMASH GenBank regions and optional BiG-SCAPE outputs", "Strain-mapping JSON" ], "other_output_formats": [ "Scored candidate links and webapp analysis views" ] }, "related_tools": [ { "slug": "gnps", "relationship": "integration", "note": "Uses GNPS molecular-network/metabolomics outputs." }, { "slug": "mibig", "relationship": "integration", "note": "Can use curated BGC/product knowledge." } ], "description": "NPLinker brings processed metabolomics and genomics together to prioritize hypotheses about which gene clusters produce detected natural products. It uses matched strain information and configurable scoring methods. Scored links are candidates for follow-up experiments, not confirmed biosynthetic assignments.", "common_uses": [ "Prioritize gene-cluster and MS-feature links across microbial strains.", "Explore paired genomics and metabolomics datasets." ], "scope": { "designed_for": [ "Hypothesis generation from paired microbial genomics and MS metabolomics." ], "not_designed_for": [ "Raw MS preprocessing or proof of a gene-cluster/product relationship." ], "requirements": [ "Matched strain identifiers connecting spectra and genomic regions.", "Supported GNPS outputs, antiSMASH results and configured dependencies, or a supported paired-omics project." ], "considerations": [ "Missing strains, silent gene clusters and inconsistent sample naming can distort links.", "Choose compatible BiG-SCAPE and MIBiG versions; the documented example uses MIBiG 3.1, so do not assume that the newest MIBiG release is interchangeable." ] }, "publications": [ { "citation": "Eldjarn GH, et al. Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions. PLOS Computational Biology 17, e1008920 (2021).", "doi": "10.1371/journal.pcbi.1008920", "type": "primary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "omicsmic", "name": "omicsMIC", "summary": "omicsMIC provides an interactive framework for simulating missingness and comparing imputation methods in mass-spectrometry-based omics matrices.", "links": { "primary": "https://github.com/WQLin8/omicsMIC", "repository": "https://github.com/WQLin8/omicsMIC", "documentation": "https://github.com/WQLin8/omicsMIC#readme" }, "resource_types": [ "web_service", "software_application" ], "functions": { "primary": "statistics", "secondary": [ "visualization_reporting" ], "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling", "multiomics" ], "interfaces": [ "web_app" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "browser", "cross_platform" ] }, "data": { "input_types": [ "feature_table" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Processed numeric omics abundance tables and simulation settings" ], "other_output_formats": [ "Imputation comparisons, evaluation summaries and interactive plots" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-02-13", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "omicsMIC provides an interactive framework for simulating missingness and comparing imputation methods in mass-spectrometry-based omics matrices. Its evaluation visualizations help users investigate how method behavior varies with dataset and missingness assumptions.", "scope": { "designed_for": [ "omicsMIC provides an interactive framework for simulating missingness and comparing imputation methods in mass-spectrometry-based omics matrices." ], "not_designed_for": [ "Establishing the true missing-value mechanism or selecting a universally optimal imputation method from one simulated benchmark." ], "requirements": [ "The hosted application or downloaded local distribution, a compatible numeric omics dataset and simulation/evaluation settings suitable for the intended comparison." ], "considerations": [ "Compare downstream effects and avoid evaluation leakage. Hosted endpoint availability and local distribution setup are separate from the published benchmarking method." ] }, "common_uses": [ "omicsMIC provides an interactive framework for simulating missingness and comparing imputation methods in mass-spectrometry-based omics matrices." ], "publications": [ { "doi": "10.1093/nargab/lqae071", "type": "primary", "citation": "omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data. NAR Genomics and Bioinformatics (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "omicspls", "name": "OmicsPLS", "summary": "Separate shared and dataset-specific variation when integrating two omics data matrices.", "description": "OmicsPLS implements O2PLS and related sparse approaches, with component selection and visualization for paired high-dimensional datasets.", "links": { "primary": "https://github.com/selbouhaddani/OmicsPLS", "documentation": "https://github.com/selbouhaddani/OmicsPLS", "repository": "https://github.com/selbouhaddani/OmicsPLS" }, "resource_types": [ "programming_package" ], "functions": { "primary": "multiomics_integration", "capabilities": [ "multivariate_statistics" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "GPL-3 R package." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Two matched numeric matrices in R" ], "other_output_formats": [ "O2PLS models, scores, loadings and plots" ] }, "publications": [ { "citation": "Bouhaddani SE, Uh HW, Jongbloed G, et al. Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics 19, 371 (2018).", "type": "primary", "doi": "10.1186/s12859-018-2371-3" } ], "common_uses": [ "Relate metabolomics to another omics layer measured in the same samples.", "Inspect joint and data-specific patterns." ], "scope": { "designed_for": [ "Relate metabolomics to another omics layer measured in the same samples." ], "not_designed_for": [ "Establishing causal regulation from shared statistical variation." ], "requirements": [ "Two numeric datasets with matched samples and appropriate scaling." ], "considerations": [ "Use cross-validation and inspect confounding; sample alignment and preprocessing strongly affect joint components." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metabosignal", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "openchrom", "name": "OpenChrom", "summary": "Inspect and process chromatographic and mass-spectrometric data in a desktop workbench.", "description": "OpenChrom provides an extensible graphical environment for chromatogram visualization, signal processing and analytical data exchange, with a strong GC-MS heritage.", "links": { "primary": "https://github.com/Openchrom/openchrom", "documentation": "https://github.com/Openchrom/openchrom", "repository": "https://github.com/Openchrom/openchrom" }, "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "capabilities": [ "peak_detection" ] }, "platforms": [ "gc_ms" ], "interfaces": [ "desktop_gui" ], "access": { "model": "open_source", "notes": "Open-source core; check separate terms for commercial extensions." }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "spectra", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Chromatogram files via installed import plug-ins" ], "other_output_formats": [ "Processed chromatograms and analysis reports" ] }, "publications": [ { "citation": "Wenig P, Odermatt J. OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data. BMC Bioinformatics 11, 405 (2010).", "type": "primary", "doi": "10.1186/1471-2105-11-405" } ], "common_uses": [ "Review chromatograms interactively.", "Build processing workflows using compatible extensions." ], "scope": { "designed_for": [ "Review chromatograms interactively." ], "not_designed_for": [ "Assuming that every vendor format or extension is included in the core installation." ], "requirements": [ "A desktop installation with the appropriate import and processing plug-ins." ], "considerations": [ "Confirm plug-in availability and format support before selecting the tool for an instrument workflow." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "ms-dial", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "openms", "name": "OpenMS", "summary": "An open-source C++ library, command-line toolkit, and Python interface for reproducible mass spectrometry data processing in metabolomics and proteomics.", "links": { "primary": "https://openms.de/", "homepage": "https://openms.de/", "repository": "https://github.com/OpenMS/OpenMS", "documentation": "https://openms.readthedocs.io/", "tutorial": "https://pyopenms.readthedocs.io/" }, "resource_types": [ "programming_package", "software_application", "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "data_conversion", "preprocessing", "quantification", "annotation_identification", "visualization_reporting" ], "capabilities": [ "peak_detection", "retention_time_alignment", "gap_filling", "provenance_tracking" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "targeted", "untargeted", "metabolite_profiling" ], "interfaces": [ "command_line", "python_package", "desktop_gui", "knime", "workflow_language" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "The software license applies to the code; separately acquired libraries, vendor readers and hosted services may have their own terms." }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform", "containers" ], "programming_languages": [ "C++", "Python" ], "package_managers": [ "Conda", "PyPI" ], "containers": [ "Docker" ], "api_available": true }, "data": { "input_types": [ "raw_instrument_data", "spectra", "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "spectra", "compound_list", "report" ], "input_formats": [ "mzml", "mzxml", "mgf", "csv_tsv" ], "output_formats": [ "mzml", "mztab_m", "csv_tsv" ], "other_input_formats": [ "mzData" ], "other_output_formats": [ "featureXML", "consensusXML" ] }, "publications": [ { "doi": "10.1038/s41592-024-02197-7", "citation": "Pfeuffer J, Bielow C, Wein S, et al. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat Methods 21, 365-367 (2024).", "type": "primary" } ], "common_uses": [ "Assemble reproducible command-line or Python workflows for LC-MS data processing.", "Convert, inspect, detect, align, quantify, and annotate mass spectrometry features.", "Inspect featureXML or consensusXML intermediates and export a table for downstream interpretation." ], "related_tools": [ { "slug": "xcms", "relationship": "alternative" }, { "slug": "gnps", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-04" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-21", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "Reproducible LC-MS processing with explicit intermediate files and reusable workflow parameters.", "Custom pipelines that combine mass-spectrometry algorithms with Python or C++ analysis." ], "not_designed_for": [ "A single automatic analysis with universally appropriate parameters for every metabolomics experiment." ], "requirements": [ "Install OpenMS or pyOpenMS and the dependencies of the selected workflow; graphical workflow systems are separate installations.", "Convert proprietary data to an accepted open format when the selected tool cannot read the original file." ], "considerations": [ "Feature detection, alignment and grouping need parameters appropriate to chromatography, resolution and sample complexity.", "Keep workflow files, parameter files, software versions and reference databases alongside the exported results.", "Select the metabolomics-specific workflow and inspect its intermediate results rather than applying proteomics defaults unchanged." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia" ], "ion_mobility": "unspecified" }, "description": "OpenMS is a modular framework for building mass-spectrometry workflows. Researchers can combine TOPP command-line tools, inspect data with TOPPView, assemble graphical workflows, or use the C++ library and pyOpenMS in their own software. Metabolomics workflows connect feature detection, retention-time alignment, feature grouping and annotation; open formats allow intermediate results to move between tools. The selected workflow determines the supported acquisition mode and outputs." }, { "$schema": "../../schemas/tool.schema.json", "slug": "opentims", "name": "OpenTIMS", "summary": "Read Bruker timsTOF TDF data through C++, Python or R interfaces.", "links": { "primary": "https://github.com/michalsta/opentims", "repository": "https://github.com/michalsta/opentims", "documentation": "https://michalsta.github.io/opentims/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_conversion", "secondary": [], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "r_package" ], "access": { "model": "open_source", "notes": "MIT core with third-party notices. The optional Bruker bridge distributes proprietary conversion binaries under separate terms; current open-source conversion can also be selected.", "license": "MIT" }, "technical": { "operating_systems": [ "linux", "macos", "windows" ], "programming_languages": [ "C++", "Python", "R" ] }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "spectra" ], "input_formats": [ "vendor_raw" ], "other_output_formats": [ "NumPy arrays, R data frames or C++ data structures with spectral coordinates" ] }, "publications": [ { "doi": "10.1021/acs.jproteome.0c00962", "type": "primary", "citation": "OpenTIMS, TimsPy, and TimsR: Open and Easy Access to timsTOF Raw Data. Journal of Proteome Research (2021)." } ], "common_uses": [ "Read timsTOF TDF data into Python or R for downstream analysis." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-25", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Programmatic access to Bruker timsTOF TDF data." ], "considerations": [ "Choose conversion/calibration behavior explicitly and record it. Large all-frame queries may exceed memory; selected columns or frame iteration can reduce resource use." ], "not_designed_for": [ "Complete metabolomics preprocessing, molecular annotation or universal support for all Bruker data formats." ], "requirements": [ "A TDF directory containing both analysis.tdf and analysis.tdf_bin, and an appropriate Python/R installation or C++ build environment. Source builds need the documented compiler and library dependencies." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "unspecified" ], "ion_mobility": "supported" }, "description": "OpenTIMS is a data-access library for Bruker timsTOF TDF files with Python and R bindings. It exposes frame, scan, time-of-flight and intensity data, plus calibrated m/z and inverse-mobility coordinates through the documented conversion routes." }, { "$schema": "../../schemas/tool.schema.json", "slug": "orbifragsnets", "name": "OrbiFragsNets", "summary": "OrbiFragsNets annotates Orbitrap data-dependent MS2 peaks with candidate fragment formulas using peak-specific uncertainty and chemical-consistency networks.", "links": { "primary": "https://github.com/EdwinChingate/OrbiFragsNets", "repository": "https://github.com/EdwinChingate/OrbiFragsNets", "documentation": "https://github.com/EdwinChingate/OrbiFragsNets#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "molecular_networking", "spectral_analysis" ], "capabilities": [ "ms2_processing" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "compound_list", "statistical_results" ], "other_input_formats": [ "Profile-mode Orbitrap mzML with precursor/formula constraints" ], "other_output_formats": [ "Annotated MS2 fragment-formula networks and spectral result tables" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-05-29", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "OrbiFragsNets annotates Orbitrap data-dependent MS2 peaks with candidate fragment formulas using peak-specific uncertainty and chemical-consistency networks. Its workflow extracts profile-derived spectral information before selecting consistent fragment-annotation networks.", "scope": { "designed_for": [ "OrbiFragsNets annotates Orbitrap data-dependent MS2 peaks with candidate fragment formulas using peak-specific uncertainty and chemical-consistency networks." ], "not_designed_for": [ "Universal centroid-spectrum annotation or full molecular-structure confirmation from a selected fragment network." ], "requirements": [ "Python dependencies and compatible profile-mode Orbitrap mzML data with precursor information and appropriate elemental/formula constraints." ], "considerations": [ "Profile information used for uncertainty is lost after centroiding. Elemental bounds and precursor constraints determine the formula space and must match the experiment." ] }, "common_uses": [ "OrbiFragsNets annotates Orbitrap data-dependent MS2 peaks with candidate fragment formulas using peak-specific uncertainty and chemical-consistency networks." ], "publications": [ { "doi": "10.1016/j.mex.2023.102257", "type": "primary", "citation": "OrbiFragsNets. A tool for automatic annotation of orbitrap MS2 spectra using networks grade as selection criteria. MethodsX (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "paccs", "name": "PACCS", "summary": "PACCS predicts CCS from molecular conformers using voxel-projected area, molecular-graph information, mass-to-charge ratio and adduct encoding.", "links": { "primary": "https://github.com/yuxuanliao/PACCS", "repository": "https://github.com/yuxuanliao/PACCS", "documentation": "https://github.com/yuxuanliao/PACCS#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "annotation_identification" ], "capabilities": [ "ccs_prediction" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package", "notebook" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "BSD-3-Clause source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "Molecular SMILES/adduct tables, generated conformers and model weights" ], "other_output_formats": [ "Predicted CCS tables and model-training artifacts" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-03-05", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "PACCS predicts CCS from molecular conformers using voxel-projected area, molecular-graph information, mass-to-charge ratio and adduct encoding. The Python/notebook workflow includes conformer generation, model training and database-scale prediction.", "scope": { "designed_for": [ "PACCS predicts CCS from molecular conformers using voxel-projected area, molecular-graph information, mass-to-charge ratio and adduct encoding." ], "not_designed_for": [ "Experimental CCS calibration or an estimate independent of conformer and adduct assumptions." ], "requirements": [ "Python with RDKit, PyTorch and PyG, SMILES/adduct inputs and a compatible trained model; conformer generation and geometric feature calculation are required preprocessing steps." ], "considerations": [ "Conformer generation, geometry optimization and training-domain coverage influence predictions. Compare with experimental data for the intended ion species and drift-gas conditions." ] }, "common_uses": [ "PACCS predicts CCS from molecular conformers using voxel-projected area, molecular-graph information, mass-to-charge ratio and adduct encoding." ], "publications": [ { "doi": "10.1002/cem.70040", "type": "primary", "citation": "Accurate and Rational Collision Cross Section Prediction Using Voxel‐Projected Area and Deep Learning. Journal of Chemometrics (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "paintomics", "name": "PaintOmics", "summary": "Visualize metabolomics alongside other omics layers on biological pathways.", "description": "PaintOmics maps processed metabolite and other omics measurements onto KEGG, Reactome and MapMan pathways for integrated enrichment and visualization.", "links": { "primary": "https://paintomics.org/", "repository": "https://github.com/ConesaLab/PaintOmics", "documentation": "https://github.com/ConesaLab/PaintOmics#readme" }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "resource_types": [ "software_application" ], "functions": { "primary": "pathway_interpretation", "capabilities": [ "pathway_enrichment", "network_visualization" ] }, "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 license verified in the official repository. External pathway databases and optional AI services have separate terms; no service execution test." }, "data": { "input_types": [ "feature_table", "compound_list", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "other_input_formats": [ "Processed omics matrices with compatible metabolite/gene identifiers" ], "other_output_formats": [ "Pathway visualizations and enrichment results" ] }, "common_uses": [ "Visualize metabolomics alongside other omics layers on biological pathways." ], "scope": { "designed_for": [ "PaintOmics maps processed metabolite and other omics measurements onto KEGG, Reactome and MapMan pathways for integrated enrichment and visualization." ], "not_designed_for": [ "Raw MS processing or confirmation of metabolite identities." ], "requirements": [ "Processed measurements with mapped identifiers and compatible experimental conditions across omics layers.", "A supported organism/pathway database; ambiguous metabolite mappings require review." ], "considerations": [ "This entry describes the published pathway-analysis core. The current release adds an AI interpretation agent; that extension was not independently validated here.", "Enrichment and pathway co-occurrence do not demonstrate causal regulation or metabolic flux." ] }, "publications": [ { "doi": "10.1093/nar/gkac352", "type": "primary", "citation": "PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases. Nucleic Acids Research (2022)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "analysis_types": [ "multiomics" ], "aliases": [ "PaintOmics 4" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "paramounter", "name": "Paramounter", "summary": "Estimate LC-MS processing parameters from measured data for downstream feature extraction.", "description": "Paramounter supplies R scripts for parameter estimation before feature extraction. The checked script reads mzXML through MSnbase and requires XCMS. Outputs guide processing settings; they are not validated metabolite identifications.", "links": { "primary": "https://github.com/HuanLab/Paramounter", "repository": "https://github.com/HuanLab/Paramounter", "documentation": "https://github.com/HuanLab/Paramounter" }, "platforms": [ "lc_ms" ], "interfaces": [ "command_line" ], "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "preprocessing", "capabilities": [ "peak_detection" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "License checked against official package metadata or repository. Local setup requires the documented dependencies; no installation or hosted-service availability test was performed." }, "data": { "input_types": [ "spectra" ], "output_types": [ "report" ], "other_input_formats": [ "mzXML files read by the supplied R scripts" ], "other_output_formats": [ "Parameter estimates and diagnostic plots for configuring downstream processing" ] }, "common_uses": [ "Estimate mass-tolerance and peak-processing settings." ], "scope": { "designed_for": [ "Estimate LC-MS processing parameters from measured data for downstream feature extraction." ], "not_designed_for": [ "Confirming compound identity or replacing study design and scientific review." ], "requirements": [ "R with XCMS, MSnbase and the other script dependencies; edit example file paths and review the supplied manual." ], "considerations": [ "The repository warns that permissive optimized peak thresholds may increase false positives or cause crashes.", "The available implementation is a script workflow, not a verified installable R package." ] }, "publications": [ { "doi": "10.1021/acs.analchem.1c04758", "type": "primary", "citation": "Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a “White Box”. Analytical Chemistry (2022)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate verified against official documentation, source/package metadata and primary-publication DOI/title/year on 2026-10-02. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" }, "related_tools": [ { "slug": "xcms", "relationship": "integration", "note": "The checked scripts use XCMS during parameter estimation." }, { "slug": "msnbase", "relationship": "integration", "note": "The input script uses readMSData from MSnbase." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "patroon", "name": "patRoon", "summary": "Assemble reproducible non-target screening workflows for environmental mass spectrometry in R.", "description": "patRoon connects feature extraction, suspect screening, formula and compound annotation, transformation-product analysis and reporting through a common interface.", "links": { "primary": "https://github.com/rickhelmus/patRoon", "documentation": "https://github.com/rickhelmus/patRoon", "repository": "https://github.com/rickhelmus/patRoon" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "capabilities": [ "provenance_tracking", "structure_database_search" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "GPL-3.0 platform; integrated software and databases retain their own terms." }, "data": { "input_types": [ "spectra", "sample_metadata" ], "output_types": [ "feature_table", "compound_list", "report" ], "input_formats": [ "mzml" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "citation": "Helmus R, Ter Laak TL, van Wezel AP, et al. patRoon: open source software platform for environmental mass spectrometry based non-target screening. J Cheminform 13, 1 (2021).", "type": "primary", "doi": "10.1186/s13321-020-00477-w" } ], "common_uses": [ "Screen environmental samples for suspect compounds.", "Compare alternative processing algorithms in a scripted workflow." ], "scope": { "designed_for": [ "Screen environmental samples for suspect compounds." ], "not_designed_for": [ "Guaranteeing identity or toxicity from a predicted candidate." ], "requirements": [ "R plus the external programs required by the selected algorithms." ], "considerations": [ "Record both patRoon and component versions; external annotation services can require separate accounts or licenses." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "openms", "relationship": "complementary" }, { "slug": "metfrag", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "pcpfm", "name": "Python-Centric Pipeline for Metabolomics", "acronym": "PCPFM", "summary": "An open Python workflow for LC-MS metabolomics that converts supported raw data, performs feature extraction with Asari, applies QC and normalization, groups empirical compounds, and supports MS1/MS2 annotation.", "links": { "primary": "https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics", "repository": "https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics", "documentation": "https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "data_conversion", "preprocessing", "quality_control", "normalization", "annotation_identification" ], "capabilities": [ "provenance_tracking", "peak_detection", "blank_filtering", "batch_effect_correction", "adduct_isotope_grouping", "library_matching" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "command_line", "python_package" ], "access": { "model": "unknown", "notes": "The LICENSE contains MIT terms preceded by wording that release under MIT is planned. Access is conservatively marked unclear pending clarification; downloaded annotation resources have separate licenses." }, "technical": { "operating_systems": [ "linux", "macos", "windows" ], "programming_languages": [ "Python" ], "package_managers": [ "PyPI" ] }, "data": { "input_types": [ "raw_instrument_data", "sample_metadata" ], "output_types": [ "feature_table", "compound_list", "report" ], "input_formats": [ "vendor_raw", "mzml", "csv_tsv" ], "output_formats": [ "csv_tsv", "json" ] }, "publications": [ { "doi": "10.1371/journal.pcbi.1011912", "type": "primary", "citation": "Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. PLOS Computational Biology (2024)." } ], "common_uses": [ "Run a reproducible LC-MS metabolomics workflow from raw or mzML data to curated feature tables.", "Combine feature extraction, QC, normalization, empirical-compound grouping, and annotation in one Python workflow." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-26", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "End-to-end, Python-centric preprocessing and annotation of LC-MS metabolomics studies." ], "considerations": [ "Inspect sample-to-file mapping and QC/blank labels. Annotation depends on chosen empirical-compound rules and reference coverage, and downloaded resources are not automatically redistributable." ], "not_designed_for": [ "Universal vendor-raw support or confirmed metabolite identification from a mass/library annotation alone." ], "requirements": [ "Python dependencies, centroid mzML data and a sequence CSV with sample names and paths; Thermo raw conversion additionally needs a configured converter and its runtime." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "unspecified" ], "ion_mobility": "unspecified" }, "description": "The Python-Centric Pipeline for Metabolomics organizes sample metadata, Asari-based preprocessing, QC and empirical-compound annotation in a common data model. Its command-line stages preserve intermediate analyses and export tables for downstream interpretation." }, { "$schema": "../../schemas/tool.schema.json", "slug": "peakbot", "name": "PeakBot", "summary": "Detect chromatographic peaks in profile-mode LC-HRMS data using machine learning.", "description": "PeakBot learns local signal patterns to distinguish chromatographic peaks from background and other structures in profile-mode mass-spectrometry data.", "links": { "primary": "https://github.com/christophuv/PeakBot", "documentation": "https://github.com/christophuv/PeakBot", "repository": "https://github.com/christophuv/PeakBot" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "peak_detection" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "access": { "model": "source_available", "notes": "Code and example environments are public; consult the repository license and model terms." }, "data": { "input_types": [ "spectra" ], "output_types": [ "feature_table" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "LC-HRMS profile-mode data" ], "other_output_formats": [ "Peak detection results" ] }, "publications": [ { "citation": "Bueschl C, Doppler M, Varga E, et al. PeakBot: machine-learning-based chromatographic peak picking. Bioinformatics 38, 3422-3428 (2022).", "type": "primary", "doi": "10.1093/bioinformatics/btac344" } ], "common_uses": [ "Extract candidate peaks from profile-mode LC-HRMS runs.", "Evaluate learned peak detection for a new chromatographic method." ], "scope": { "designed_for": [ "Extract candidate peaks from profile-mode LC-HRMS runs." ], "not_designed_for": [ "Direct use as a chemical-identification engine." ], "requirements": [ "Profile-mode data, a compatible Python environment and an appropriate trained model." ], "considerations": [ "Training data and instrument characteristics affect transferability; inspect predictions on representative samples." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "asari", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "peakdecoder", "name": "PeakDecoder", "summary": "Use machine learning to support metabolite identification and error-rate estimation in multidimensional LC-IM-MS-DIA data.", "description": "PeakDecoder is a workflow for metabolite annotation and profiling in liquid-chromatography, ion-mobility and data-independent-acquisition mass spectrometry. It uses precursor and fragment-ion co-elution and co-mobility information together with machine learning to score candidate metabolite assignments and estimate false-discovery rates.", "links": { "primary": "https://github.com/EMSL-Computing/PeakDecoder", "repository": "https://github.com/EMSL-Computing/PeakDecoder", "documentation": "https://github.com/EMSL-Computing/PeakDecoder" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning", "quality_control" ], "capabilities": [ "identification_confidence", "ms2_processing" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "command_line" ], "access": { "model": "open_source", "license": "BSD-2-Clause", "notes": "Source code, example data and workflow instructions are publicly available in the project repository." }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "compound_list", "statistical_results" ], "input_formats": [ "csv_tsv", "msp", "other" ], "output_formats": [ "csv_tsv", "other" ], "other_input_formats": [ "MS-DIAL peak lists and Skyline reports" ], "other_output_formats": [ "PeakDecoder scores and FDR thresholds" ] }, "publications": [ { "doi": "10.1038/s41467-023-37031-9", "citation": "Bilbao A, et al. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nature Communications (2023).", "type": "primary" } ], "common_uses": [ "Score metabolite identifications in LC-IM-MS-DIA datasets using co-elution and co-mobility information.", "Estimate false-discovery-rate thresholds for candidate metabolite identifications." ], "scope": { "designed_for": [ "Multidimensional LC-IM-MS-DIA metabolite-identification workflows with precursor and fragment-ion traces." ], "not_designed_for": [ "General-purpose preprocessing of arbitrary LC-MS datasets or de novo structure elucidation." ], "requirements": [ "Upstream feature and fragment-ion processing plus targeted extraction of precursor and fragment traces." ], "considerations": [ "Model performance depends on the training data and the quality of chromatographic and ion-mobility measurements." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Added from the 2026 computational-metabolomics review population and Springer methods coverage; checked against the official PeakDecoder repository and primary publication on 10 September 2026." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dia" ], "ion_mobility": "required" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "peakpanther", "name": "peakPantheR", "aliases": [], "summary": "R package for targeted extraction and integration of predefined metabolic features across LC-MS profiling files.", "links": { "primary": "https://bioconductor.org/packages/release/bioc/html/peakPantheR.html", "repository": "https://github.com/phenomecentre/peakPantheR", "issue_tracker": "https://github.com/phenomecentre/peakPantheR/issues", "documentation": "https://phenomecentre.github.io/peakPantheR.github.io/", "homepage": "https://phenomecentre.github.io/peakPantheR.github.io/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [], "capabilities": [ "peak_detection", "chromatogram_building" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "targeted" ], "interfaces": [ "r_package", "web_app" ], "access": { "model": "open_source", "license": "GPL-3", "notes": "Open-source Bioconductor R package. Match R and Bioconductor releases and consult the selected release requirements. The Shiny GUI runs from the installed package; it is not a separate hosted analysis service." }, "publications": [ { "doi": "10.1093/bioinformatics/btab433", "type": "primary", "citation": "Wolfer AM, et al. peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets. Bioinformatics 37, 4886-4888 (2021)." } ], "common_uses": [ "Extract a consistent predefined feature panel across LC-MS files.", "Review chromatographic fits and refine target integration windows." ], "scope": { "designed_for": [ "Targeted extraction from predefined retention-time and m/z regions in profiling data." ], "not_designed_for": [ "Discovery of all unknown features or de novo metabolite identification." ], "requirements": [ "A target annotation table with feature identifiers and retention-time/mass windows.", "MS files supported by the underlying R readers and a compatible R/Bioconductor installation." ], "considerations": [ "Chromatographic drift and coelution can compromise extraction within fixed windows.", "Inspect fitting diagnostics and retain target definitions and parameter changes.", "A fitted peak does not replace reference-standard evidence for its assigned identity." ] }, "related_tools": [ { "slug": "msnbase", "relationship": "integration", "note": "Used in the package data-reading ecosystem alongside mzR; check release compatibility." } ], "credits": [ { "name": "Gonçalo Correia", "role": "developer", "orcid": "0000-0001-8271-9294" } ], "provenance": { "submitted_by": "developer", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/17", "developer_verified": false, "submitter_name": "Gonçalo Correia", "public_attribution_consent": true, "submitter_orcid": "0000-0001-8271-9294", "notes": "Recovered from the corrected public submission after its original ORCID parsing failure. Bioconductor, official project documentation and DOI metadata reviewed on 2026-09-12. No software execution or independent benchmarking performed. Contributor consent and submitted identity preserved; account ownership was not independently authenticated." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_verified": "2026-09-12", "last_editorial_review": "2026-09-12" }, "description": "peakPantheR searches specified retention-time and m/z regions, fits chromatographic peaks and reports integration results with diagnostic plots. It supports processing many files in parallel and reviewing parameters through a local Shiny interface. The target list supplies the expected feature annotations; extraction does not independently establish compound identity.", "data": { "input_types": [ "spectra", "compound_list" ], "input_formats": [ "mzml" ], "other_input_formats": [ "Target feature table with retention-time and m/z windows" ], "output_types": [ "feature_table", "report" ], "output_formats": [ "csv_tsv" ], "other_output_formats": [ "Diagnostic chromatographic plots and R annotation objects" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "plantmasst", "name": "plantMASST", "summary": "A domain-specific GNPS search and exploration resource for contextualizing query MS/MS spectra across taxonomically defined plant metabolomics datasets.", "description": "plantMASST searches query tandem mass spectra against a public collection of taxonomically curated plant extracts and displays matches within plant taxonomy. Its current Explorer interface can also browse the indexed plant collection without a query spectrum, inspect files for individual taxa, and launch selected public files into GNPS2 molecular-networking workflows. The resource is community-driven and uses harmonized taxonomy and repository metadata.", "links": { "primary": "https://masst.gnps2.org/plantmasst/", "web_app": "https://masst.gnps2.org/plantmasst/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/plantmasst/", "repository": "https://github.com/robinschmid/microbe_masst" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "secondary": [ "spectral_analysis", "data_management_sharing" ], "capabilities": [ "repository_scale_search", "metadata_contextualization", "library_matching", "interactive_dashboard" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "web_app", "python_package" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.1101/2024.05.13.593988", "citation": "Gomes PWP, Mannochio-Russo H, Schmid R, et al. plantMASST - Community-driven chemotaxonomic digitization of plants. bioRxiv (2024). Preprint; not peer reviewed.", "type": "primary" } ], "common_uses": [ "Search an MS/MS spectrum across taxonomically defined plant extracts.", "Explore which plant taxa and public files are represented in the current reference collection.", "Launch selected public plant files into GNPS2 molecular-networking analysis.", "Browse the Explorer collection and select plant data for subsequent GNPS analysis." ], "scope": { "designed_for": [ "Chemotaxonomic contextualization of known and unknown plant-associated MS/MS spectra." ], "requirements": [ "A query spectrum/USI for search mode, or no query for the Explorer view." ], "considerations": [ "The associated methodological publication is currently a preprint; the live resource continues to evolve as community plant data are deposited.", "Taxonomic and spectral coverage is not uniform across plant diversity.", "Keep the query and selected taxonomy context; investigate the original plant extracts before interpreting a chemotaxonomic pattern." ], "not_designed_for": [ "Establishing taxon-specific biosynthesis or absence across unmeasured plant species." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "fasst", "relationship": "uses" }, { "slug": "redu", "relationship": "uses" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser", "cross_platform" ], "programming_languages": [ "Python" ], "api_available": false }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra", "sample_metadata", "report" ], "input_formats": [ "mgf" ], "output_formats": [ "csv_tsv" ], "other_input_formats": [ "Universal Spectrum Identifier (USI) or peak list" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "plsko", "name": "PLSKO", "summary": "PLSKO generates partial-least-squares-based knockoff variables and applies knockoff filtering for omics feature selection.", "links": { "primary": "https://github.com/guannan-yang/PLSKO", "repository": "https://github.com/guannan-yang/PLSKO", "documentation": "https://guannan-yang.github.io/PLSKO/PLSKO.html" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "statistics" ], "capabilities": [ "feature_selection" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling", "multiomics" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "Numeric predictor matrix, response vector and target FDR level" ], "other_output_formats": [ "Knockoff matrices, importance statistics and selected-variable objects" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-08-29", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "PLSKO generates partial-least-squares-based knockoff variables and applies knockoff filtering for omics feature selection. Its R workflow includes single and aggregated filtering routes, importance statistics and selected-variable outputs.", "scope": { "designed_for": [ "PLSKO generates partial-least-squares-based knockoff variables and applies knockoff filtering for omics feature selection." ], "not_designed_for": [ "Causal biomarker discovery or unconditional false-discovery guarantees when knockoff/model assumptions are violated." ], "requirements": [ "R and documented knockoff/mixOmics dependencies, a numeric predictor matrix, response vector and target false-discovery-rate setting; tune the knockoff construction for the data." ], "considerations": [ "Assess exchangeability and model settings, distinguish predictive importance from causal effects and keep tuning/evaluation data appropriately separated." ] }, "common_uses": [ "PLSKO generates partial-least-squares-based knockoff variables and applies knockoff filtering for omics feature selection." ], "publications": [ { "doi": "10.1093/bioinformatics/btaf475", "type": "primary", "citation": "PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection. Bioinformatics (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "pnnl-preprocessor", "name": "PNNL PreProcessor", "summary": "Preprocess multidimensional ion-mobility mass-spectrometry data with demultiplexing, smoothing, noise filtering, saturation repair, and format-conversion utilities.", "description": "PNNL PreProcessor is a desktop preprocessing utility developed for ion-mobility mass spectrometry. It supports Agilent MassHunter and UIMF data from drift-tube and SLIM IM-MS platforms and provides operations including data compression/interpolation, ion-mobility demultiplexing, multidimensional smoothing, intensity-threshold and spike filtering, saturation repair, metadata export, and selected conversions that make IM information usable in downstream LC-MS-oriented workflows.", "links": { "primary": "https://pnnl-comp-mass-spec.github.io/PNNL-PreProcessor/", "homepage": "https://pnnl-comp-mass-spec.github.io/PNNL-PreProcessor/", "repository": "https://github.com/PNNL-Comp-Mass-Spec/PNNL-PreProcessor", "documentation": "https://pnnl-comp-mass-spec.github.io/PNNL-PreProcessor/" }, "resource_types": [ "software_application" ], "functions": { "primary": "preprocessing", "secondary": [ "data_conversion", "quality_control" ], "capabilities": [ "spectral_deconvolution" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "interfaces": [ "desktop_gui" ], "access": { "model": "unknown", "notes": "The project distributes the binary free of charge, but the source code is closed because of vendor-format restrictions and use is subject to third-party library terms." }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "raw_instrument_data" ], "input_formats": [ "vendor_raw", "other" ], "output_formats": [ "vendor_raw", "other" ], "other_input_formats": [ "Agilent MassHunter .d", "UIMF" ], "other_output_formats": [ "Enhanced vendor-format MS files" ] }, "publications": [ { "doi": "10.1021/acs.jproteome.1c00425", "citation": "Bilbao A, et al. A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows. J Proteome Res (2021).", "type": "primary" }, { "doi": "10.1021/jasms.4c00220", "citation": "Stow SM, et al. Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows. J Am Soc Mass Spectrom (2024).", "type": "methods" } ], "common_uses": [ "Improve signal quality before downstream LC-IM-MS data processing.", "Demultiplex and smooth ion-mobility data and remove low-intensity or spike noise.", "Convert selected IM-MS data representations for use with downstream workflows." ], "scope": { "designed_for": [ "Preprocessing Agilent drift-tube and SLIM ion-mobility MS data before downstream omics analysis." ], "not_designed_for": [ "General-purpose metabolite annotation or statistical analysis." ], "requirements": [ "Compatible Agilent MassHunter .d or UIMF input data and a supported desktop environment." ], "considerations": [ "Saturation repair can produce incorrect results for highly convoluted elution or mobility profiles; output logs should be inspected." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Added from the 2026 computational-metabolomics review population and Springer computational-metabolomics methods coverage; scope and limitations checked against official PNNL documentation on 10 September 2026." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "dia" ], "ion_mobility": "required" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "postmn-massql", "name": "PostMN MassQL", "summary": "Run MassQL chemical-pattern queries directly on consensus spectra produced by GNPS molecular networking.", "links": { "primary": "https://massqlpostmn.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_PostMN_MassQL/" }, "resource_types": [ "web_service" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "ms2_processing", "provenance_tracking" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Publicly hosted MetaboApp. GNPS2 infrastructure is open to the academic community; its documentation directs industry users to the maintainers or UCR Technology Transfer. Hosted access does not establish a software or reference-data license." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Reviewed against the linked official app documentation on 2026-09-10. No standalone publication for this particular app was verified; the documentation citation identifies the reviewed implementation. Underlying method and application papers are not presented as dedicated app papers. No analysis job or independent benchmark was run; no developer verification is claimed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra", "report" ], "other_input_formats": [ "Public GNPS2 CMN/FBMN task ID and MassQL query text" ], "output_formats": [ "mgf", "csv_tsv" ] }, "scope": { "designed_for": [ "Chemical-pattern searching after classical or feature-based molecular networking." ], "not_designed_for": [ "Raw-file preprocessing or conclusive identification from a query hit alone." ], "requirements": [ "A completed, public GNPS2 CMN or FBMN task and a valid MassQL query." ], "considerations": [ "A query hit supports a fragmentation hypothesis; it does not validate a full structure.", "The app searches processed spectra, so upstream processing affects the available evidence.", "Retain the executed query text and upstream task identifier with exported results." ] }, "related_tools": [ { "slug": "metaboapps", "relationship": "part_of", "note": "Standalone MetaboApp that can be launched directly or from a GNPS2 task." }, { "slug": "gnps", "relationship": "integration", "note": "Consumes GNPS2 tasks or ecosystem data." }, { "slug": "massql", "relationship": "integration", "note": "Uses the MassQL query language." }, { "slug": "gnps-classical-molecular-networking", "relationship": "integration", "note": "Accepts a completed public CMN task." }, { "slug": "gnps-feature-based-molecular-networking", "relationship": "integration", "note": "Accepts a completed public FBMN task." } ], "maintenance": { "status": "active", "last_checked": "2026-09-10" }, "publications": [], "resource_citations": [ { "citation": "GNPS2 Documentation. PostMN MassQL. Accessed 10 September 2026.", "type": "documentation", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/metaboapp_PostMN_MassQL/" } ], "description": "PostMN MassQL searches the processed spectra from a completed GNPS2 networking job. Researchers can select, edit or write fragmentation-pattern queries and inspect which spectra satisfy them, alongside available library annotations.", "common_uses": [ "Screen networking results for diagnostic fragments or neutral losses.", "Export spectra satisfying selected query conditions." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "pubchem", "name": "PubChem", "summary": "Search chemical structures, identifiers, properties and contributed annotations.", "description": "PubChem integrates substance submissions, standardized compound records and bioassay information. It is useful for identifier reconciliation and candidate-reference lookup.", "links": { "primary": "https://pubchem.ncbi.nlm.nih.gov/", "documentation": "https://pubchem.ncbi.nlm.nih.gov/docs/" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public NIH resource; attribution and reuse terms can vary across contributing data sources." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "compound_list", "molecular_structures" ], "input_formats": [ "other" ], "output_formats": [ "sdf", "json" ], "other_input_formats": [ "Names, identifiers or structures" ] }, "publications": [ { "citation": "Kim S, Chen J, Cheng T, et al. PubChem 2025 update. Nucleic Acids Res 53, D1516-D1525 (2025).", "type": "primary", "doi": "10.1093/nar/gkae1059" } ], "common_uses": [ "Resolve compound identifiers and synonyms.", "Retrieve candidate structures for an annotation workflow." ], "scope": { "designed_for": [ "Resolve compound identifiers and synonyms." ], "not_designed_for": [ "Demonstrating that a compound is present in a biological sample." ], "requirements": [ "A chemical name, identifier, structure or supported search query." ], "considerations": [ "Check stereochemistry, charge and salt forms when matching records; contributed annotations retain source-specific evidence." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "chebi", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "pyeva", "name": "pyEVA", "aliases": [ "EVA" ], "summary": "A Python implementation of EVA for automated evaluation of extracted-ion chromatogram fidelity, using deep learning to classify LC-MS features as likely true or false signals.", "links": { "primary": "https://github.com/HuanLab/pyEVA", "repository": "https://github.com/HuanLab/pyEVA", "documentation": "https://github.com/HuanLab/pyEVA#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "quality_control", "secondary": [ "machine_learning" ], "capabilities": [ "feature_selection" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "command_line" ], "access": { "model": "open_source", "notes": "MIT source distribution. The cited EVA paper describes the original method; the repository documents the later Python implementation and its setup.", "license": "MIT" }, "technical": { "operating_systems": [ "windows", "macos", "linux" ], "programming_languages": [ "Python", "R" ] }, "data": { "input_types": [ "raw_instrument_data", "feature_table" ], "output_types": [ "feature_table", "report" ], "input_formats": [ "mzml", "mzxml", "csv_tsv" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1021/acs.analchem.1c01309", "type": "primary", "citation": "EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms. Analytical Chemistry (2021).", "note": "Original EVA method paper; the official pyEVA repository documents its Python reimplementation." } ], "common_uses": [ "Automatically classify extracted-ion chromatograms as higher- or lower-fidelity metabolomic features.", "Filter feature tables after chromatographic peak detection." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-02-06", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Automated feature-fidelity assessment of LC-MS extracted-ion chromatograms." ], "considerations": [ "Smoothing and peak-shape domain affect classifications. Inspect EICs and prediction outcomes rather than automatically interpreting every rejected feature as an artifact." ], "not_designed_for": [ "Molecular identification or a replacement for acquisition-aware review of low-abundance and unusual chromatographic peaks." ], "requirements": [ "The documented Python environment plus R/xcms dependencies, raw mzML or mzXML files and a CSV whose first two columns are m/z and retention time." ] }, "acquisition": { "ms_levels": [ "ms1" ], "strategies": [ "full_scan" ], "ion_mobility": "unspecified" }, "description": "pyEVA is a Python implementation of the EVA feature-fidelity workflow. It extracts chromatographic traces around supplied LC-MS features, creates EIC plots and classifies them to assist detection of false or unreliable features." }, { "$schema": "../../schemas/tool.schema.json", "slug": "pyimzml", "name": "pyimzML", "summary": "Python library for reading and writing imzML imaging mass-spectrometry data.", "description": "pyimzML exposes pixel coordinates, spectra and metadata to Python code. Its parser and writer support custom imaging-data workflows rather than providing a complete graphical analysis environment.", "links": { "primary": "https://github.com/alexandrovteam/pyimzML", "repository": "https://github.com/alexandrovteam/pyimzML", "documentation": "https://pyimzml.readthedocs.io/en/latest/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_conversion", "capabilities": [ "spectral_data_io" ] }, "platforms": [ "imaging_ms" ], "analysis_types": [ "spatial_metabolomics" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Installable Python package with source available under Apache-2.0. Included ontology-derived data have their own attribution notices." }, "data": { "input_types": [ "imaging_data" ], "input_formats": [ "imzml" ], "output_types": [ "imaging_data" ], "output_formats": [ "imzml" ], "other_output_formats": [ "Python-accessible spectra", "coordinates and metadata" ] }, "common_uses": [ "Read pixel spectra in a scripted imaging workflow.", "Write imzML output after custom transformations." ], "scope": { "designed_for": [ "Programmatic imzML input/output and interoperability." ], "not_designed_for": [ "An end-to-end MSI statistics or molecular-annotation workflow." ], "requirements": [ "Python and the package dependencies; imzML metadata and associated binary data." ], "considerations": [ "The repository specifies imzML version 1.1.0; check compatibility with the producing software.", "Preserve coordinates and relevant metadata when transforming data.", "Validate written files with the intended downstream reader." ] }, "related_tools": [ { "slug": "cardinal", "relationship": "complementary", "note": "Cardinal offers imaging analysis in R; exchanging imzML requires compatible files and is not a verified direct integration." } ], "publications": [], "resource_citations": [ { "citation": "Alexandrov Team. pyimzML repository and documentation. Accessed 11 September 2026.", "type": "documentation", "url": "https://pyimzml.readthedocs.io/en/latest/" } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official repository and parser/writer documentation reviewed on 2026-09-11. No dedicated peer-reviewed tool paper verified; no incidental usage paper added. Package installation, round-trip fidelity and interoperability were not tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-11", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "pyopenms-viz", "name": "pyOpenMS-viz", "summary": "pyOpenMS-viz extends pandas plotting for mass-spectrometry data, including chromatograms, mobilograms, spectra and two- or three-dimensional peak maps.", "links": { "primary": "https://github.com/OpenMS/pyopenms_viz", "repository": "https://github.com/OpenMS/pyopenms_viz", "documentation": "https://pyopenms-viz.readthedocs.io/en/latest/index.html" }, "resource_types": [ "programming_package" ], "functions": { "primary": "visualization_reporting", "secondary": [ "spectral_analysis" ], "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "BSD-3-Clause source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ], "package_managers": [ "PyPI" ] }, "data": { "input_types": [ "spectra", "feature_table" ], "output_types": [ "report" ], "other_input_formats": [ "pandas DataFrames with m/z, time/mobility and intensity columns" ], "other_output_formats": [ "Static or interactive chromatogram, spectrum and peak-map figures" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-27", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "pyOpenMS-viz extends pandas plotting for mass-spectrometry data, including chromatograms, mobilograms, spectra and two- or three-dimensional peak maps. It supports static matplotlib and interactive Bokeh or Plotly backends.", "scope": { "designed_for": [ "pyOpenMS-viz extends pandas plotting for mass-spectrometry data, including chromatograms, mobilograms, spectra and two- or three-dimensional peak maps." ], "not_designed_for": [ "Raw feature detection, structural identification or automatic conversion of every vendor format into a plot-ready table." ], "requirements": [ "A compatible Python environment, pandas DataFrames with the dimensions required by the plot and the selected plotting backend." ], "considerations": [ "Data preparation and column selection remain the user's responsibility. Plot types and three-dimensional support differ between backends." ] }, "common_uses": [ "pyOpenMS-viz extends pandas plotting for mass-spectrometry data, including chromatograms, mobilograms, spectra and two- or three-dimensional peak maps." ], "publications": [ { "doi": "10.1021/acs.jproteome.4c00873", "type": "primary", "citation": "pyOpenMS-viz: Streamlining Mass Spectrometry Data Visualization with pandas. Journal of Proteome Research (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "qc4metabolomics", "name": "QC4Metabolomics", "summary": "Monitor LC-MS data quality with a configurable processing service, database and dashboard.", "description": "QC4Metabolomics provides a locally deployed QC system with background processing, a MariaDB database and a Shiny interface. Its MetabolomiQCsR component supplies QC functions. Optional conversion services prepare mzML inputs.", "links": { "primary": "https://stanstrup.github.io/QC4Metabolomics/", "repository": "https://github.com/stanstrup/QC4Metabolomics", "documentation": "https://stanstrup.github.io/QC4Metabolomics/" }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "quality_control", "capabilities": [ "container_support", "interactive_dashboard" ] }, "access": { "model": "open_source", "license": "GPL-3.0-or-later", "notes": "License checked against official package metadata or repository. Local setup requires the documented dependencies; no installation or hosted-service availability test was performed." }, "data": { "input_types": [ "spectra", "sample_metadata" ], "output_types": [ "report" ], "other_input_formats": [ "mzML files plus configured file-name metadata and QC settings" ], "other_output_formats": [ "QC database records and dashboard views" ] }, "common_uses": [ "Track quality measurements across acquisitions." ], "scope": { "designed_for": [ "Monitor LC-MS data quality with a configurable processing service, database and dashboard." ], "not_designed_for": [ "Confirming compound identity or replacing study design and scientific review." ], "requirements": [ "Docker/Compose, configured data volumes, database and background workers." ], "considerations": [ "This is infrastructure to deploy and configure, not simply a public upload website.", "File naming and metadata parsing must match the local acquisition setup." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c07078", "type": "primary", "citation": "QC4Metabolomics: Real-Time and Retrospective Quality Control of Metabolomics Data. Analytical Chemistry (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate verified against official documentation, source/package metadata and primary-publication DOI/title/year on 2026-10-02. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" }, "aliases": [ "MetabolomiQCsR" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "quantyfey", "name": "QuantyFey", "summary": "Calculate concentrations from integrated LC-MS signals with calibration and drift correction.", "description": "QuantyFey is a locally run Shiny application for external-calibration quantification. It supports inspection and adjustment of calibration models and several intensity-drift correction options.", "links": { "primary": "https://github.com/CDLMarkus/QuantyFey", "repository": "https://github.com/CDLMarkus/QuantyFey", "documentation": "https://github.com/CDLMarkus/QuantyFey/blob/master/tutorial/tutorial.md" }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "resource_types": [ "software_application" ], "functions": { "primary": "quantification", "capabilities": [ "calibration_curve", "qc_drift_correction" ] }, "access": { "model": "open_source", "notes": "License checked against official repository/package metadata. Installation and execution were not tested.", "license": "GPL-3.0" }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "Integrated signal tables and configured calibration/template information" ], "other_output_formats": [ "Excel concentration and parameter tables; PDF reports" ] }, "common_uses": [ "Calculate concentrations from integrated LC-MS signals with calibration and drift correction." ], "scope": { "designed_for": [ "QuantyFey is a locally run Shiny application for external-calibration quantification. It supports inspection and adjustment of calibration models and several intensity-drift correction options." ], "not_designed_for": [ "Chromatographic peak integration from raw files." ], "requirements": [ "Integrated signals, known-concentration standards and a configured input template.", "Use a documented local R environment, Windows distribution or Linux Apptainer setup." ], "considerations": [ "Correction requires appropriate standards/control information; model selection cannot rescue an unsuitable calibration.", "The README contains platform-dependent installation routes; check the route-specific requirements." ] }, "publications": [ { "doi": "10.1016/j.aca.2025.344571", "type": "primary", "citation": "QuantyFey: An open-source tool for targeted LC-MS quantification with integrated drift correction. Analytica Chimica Acta (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" }, "analysis_types": [ "targeted" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ralps", "name": "RALPS", "summary": "An R-based regularized adversarial-learning method for harmonizing metabolomics batches using shared reference samples while preserving biological similarity structure.", "links": { "primary": "https://github.com/zamboni-lab/RALPS", "repository": "https://github.com/zamboni-lab/RALPS", "documentation": "https://github.com/zamboni-lab/RALPS#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "normalization", "secondary": [ "machine_learning" ], "capabilities": [ "batch_effect_correction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Abundance CSV, batch/group/benchmark CSV and configuration CSV" ], "other_output_formats": [ "Normalized abundance CSV, model checkpoints and evaluation plots" ] }, "publications": [ { "doi": "10.1093/bioinformatics/btad096", "type": "primary", "citation": "Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data. Bioinformatics (2023)." } ], "common_uses": [ "Harmonize multiple metabolomics batches using embedded shared reference samples." ], "maintenance": { "status": "unclear", "repository_last_activity": "2023-06-26", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "An R-based regularized adversarial-learning method for harmonizing metabolomics batches using shared reference samples while preserving biological similarity structure." ], "not_designed_for": [ "Replacing the preceding within-batch drift correction or inferring batch effects without the required reference information." ], "requirements": [ "The documented Python/PyTorch environment, an abundance CSV, batch/replicate metadata and configuration file; reference replicate arrangements must connect the batches being harmonized." ], "considerations": [ "Training and parameter selection affect the reconstructed data. Check reference-sample agreement, variance and biological signal preservation; distinguish regularization replicates from evaluation benchmarks." ] }, "description": "RALPS harmonizes batches of quantified metabolomics data using regularized adversarial learning and shared reference measurements. It targets inter-batch variation after each batch has first been normalized for within-batch problems." }, { "$schema": "../../schemas/tool.schema.json", "slug": "ramclustr", "name": "RAMClustR", "summary": "Cluster related mass-spectrometry features and export compound-level spectra and abundances.", "description": "RAMClustR groups features using retention and abundance relationships. Its workflow supports feature filtering, QC normalization and spectral export for subsequent annotation.", "links": { "primary": "https://github.com/cbroeckl/RAMClustR", "documentation": "https://github.com/cbroeckl/RAMClustR", "repository": "https://github.com/cbroeckl/RAMClustR" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "spectral_deconvolution", "adduct_isotope_grouping" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "access": { "model": "source_available", "notes": "Source and environment specifications are public; review repository license terms before redistribution." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "spectra", "feature_table" ], "input_formats": [ "csv_tsv" ], "output_formats": [ "msp" ] }, "publications": [ { "citation": "Broeckling CD, Afsar FA, Neumann S, et al. RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem 86, 6812-6817 (2014).", "type": "primary", "doi": "10.1021/ac501530d" } ], "common_uses": [ "Reduce redundant features into compound-related clusters.", "Export reconstructed spectra for library matching." ], "scope": { "designed_for": [ "Reduce redundant features into compound-related clusters." ], "not_designed_for": [ "Treating every cluster as a pure, independently identified metabolite." ], "requirements": [ "A feature table with m/z, retention time and sample information, or supported XCMS objects." ], "considerations": [ "Correlated coeluting compounds can merge; review clustering thresholds and reconstructed spectra." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "camera", "relationship": "complementary" }, { "slug": "gnps", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ramp-db", "name": "RaMP-DB", "acronym": "RaMP", "summary": "Integrated metabolite, gene/protein, reaction, ontology and pathway resource for metabolomics and multi-omics pathway analysis.", "description": "RaMP-DB integrates metabolite and gene/protein identifiers with pathway, reaction, chemical and ontology annotations. Researchers can query the resource through a browser, API or R package and perform downstream enrichment on MS-derived metabolite lists. Database coverage, mapping and the chosen background determine what can be interpreted.", "links": { "primary": "https://rampdb.nih.gov/", "repository": "https://github.com/ncats/RaMP-DB", "documentation": "https://github.com/ncats/RaMP-DB#installation-instructions" }, "resource_types": [ "database_knowledgebase", "programming_package", "api_service" ], "functions": { "primary": "pathway_interpretation", "secondary": [ "reference_data_search", "multiomics_integration" ], "capabilities": [ "pathway_enrichment" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted", "targeted", "multiomics" ], "interfaces": [ "web_app", "r_package", "api" ], "access": { "model": "open_source", "license": "GPL-2", "notes": "The R package declares GPL-2 and provides local access through a downloaded SQLite database. Public web and API interfaces are also available. The software license does not replace the terms of contributing source databases; record both package and database versions." }, "data": { "input_types": [ "compound_list", "pathway_data" ], "output_types": [ "pathway_data", "statistical_results" ], "input_formats": [ "csv_tsv" ], "output_formats": [ "csv_tsv", "json" ], "other_input_formats": [ "Metabolite or gene identifiers with source prefixes; browser or API queries" ] }, "publications": [ { "citation": "Zhang B, Hu S, Baskin E, et al. RaMP: a comprehensive relational database of metabolomics pathways for pathway enrichment analysis of genes and metabolites. Metabolites 8, 16 (2018).", "type": "primary", "doi": "10.3390/metabo8010016" }, { "citation": "Braisted J, et al. RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes. Bioinformatics 39, btac726 (2023).", "doi": "10.1093/bioinformatics/btac726", "type": "methods" } ], "common_uses": [ "Map metabolite identifiers across pathway resources.", "Perform pathway enrichment with metabolites or combined gene/metabolite lists.", "Retrieve pathway, reaction and ontology context programmatically." ], "scope": { "designed_for": [ "Biological interpretation and identifier harmonization after metabolite or gene-level results are available." ], "not_designed_for": [ "Raw mass-spectrometry preprocessing or primary metabolite identification." ], "considerations": [ "Pathway results depend on identifier mapping, source-database coverage, background definition and the biological context of the study.", "Inspect unmapped and multiply mapped identifiers; enrichment does not validate the original MS annotation." ], "requirements": [ "Mapped metabolite identifiers with database prefixes, or supported gene/protein identifiers.", "For local R use, the package dependencies and a cached RaMP database; use a recorded database version." ] }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official project documentation and direct tool publication metadata reviewed on 2026-09-12. No account creation, data upload, installation or independent benchmarking performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-12", "updated_at": "2026-09-12", "last_editorial_review": "2026-09-12", "last_verified": "2026-09-12" }, "related_tools": [ { "slug": "metaboanalyst", "relationship": "complementary", "note": "Complementary pathway context; identifier coverage and analysis methods differ." }, { "slug": "reactome", "relationship": "complementary", "note": "Complementary pathway context; identifier coverage and analysis methods differ." }, { "slug": "wikipathways", "relationship": "complementary", "note": "Complementary pathway context; identifier coverage and analysis methods differ." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "rapid-qc-ms", "name": "Rapid QC-MS", "summary": "Monitor LC-MS acquisition quality through an interactive dashboard.", "description": "Rapid QC-MS tracks internal-standard metrics and reference samples during or after acquisition, with configurable QC checks and notifications.", "links": { "primary": "https://czbiohub-sf.github.io/Rapid-QC-MS/", "repository": "https://github.com/czbiohub-sf/Rapid-QC-MS", "documentation": "https://czbiohub-sf.github.io/Rapid-QC-MS/" }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app", "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "quality_control", "capabilities": [ "interactive_dashboard" ] }, "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "The repository license contains BSD-3-Clause terms. Third-party vendor readers and optional cloud services have separate requirements. Not execution-tested." }, "data": { "input_types": [ "raw_instrument_data", "sample_metadata", "spectral_library" ], "output_types": [ "report" ], "other_input_formats": [ "Vendor raw data and acquisition sequence CSV", "Configured reference samples and internal standards" ], "other_output_formats": [ "Dashboard QC metrics and plots; optional notification and cloud-sync outputs" ] }, "common_uses": [ "Monitor LC-MS acquisition quality through an interactive dashboard." ], "scope": { "designed_for": [ "Rapid QC-MS tracks internal-standard metrics and reference samples during or after acquisition, with configurable QC checks and notifications." ], "not_designed_for": [ "Automatic validation of biological conclusions or a replacement for experiment design and independent QC review." ], "requirements": [ "Windows acquisition/processing setup with Python, MSConvert and MS-DIAL; configure standards and thresholds for the assay." ], "considerations": [ "Extensive testing documented primarily for Thermo instruments, acquisition sequences and RAW files; other vendors may require adaptation.", "Notification and cloud-sync integrations require separate configuration." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c00786", "type": "primary", "citation": "Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control. Analytical Chemistry (2024)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "analysis_types": [ "untargeted" ], "related_tools": [ { "slug": "ms-dial", "relationship": "dependency", "note": "The documented processing workflow requires MS-DIAL." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "rassp", "name": "RASSP", "summary": "A research implementation for rapid prediction of electron-ionization mass spectra from molecular structures using neural-network models.", "links": { "primary": "https://github.com/thejonaslab/rassp-public", "repository": "https://github.com/thejonaslab/rassp-public", "documentation": "https://github.com/thejonaslab/rassp-public#readme" }, "resource_types": [ "programming_package", "api_service" ], "functions": { "primary": "spectral_analysis", "secondary": [ "machine_learning" ], "capabilities": [ "in_silico_spectra" ] }, "platforms": [ "gc_ms" ], "interfaces": [ "python_package", "api" ], "access": { "model": "source_available", "notes": "Public research implementation and linked model files; no explicit repository-wide software license was identified. README container routes are marked TBD and are not advertised here as available deployments." }, "technical": { "operating_systems": [ "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "spectra" ], "other_input_formats": [ "Molecular SMILES/InChI lists or prepared molecular Parquet datasets" ], "other_output_formats": [ "Predicted EI spectra as text or SQLite/pickled spectrum records" ] }, "common_uses": [ "Predict EI spectra from molecular structures for GC-MS annotation research." ], "maintenance": { "status": "unclear", "repository_last_activity": "2023-01-25", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "RASSP predicts electron-ionization mass spectra from candidate molecular structures using subset/formula-based neural models. Its research implementation includes pretrained-model inference, training and library-matching evaluation code.", "scope": { "designed_for": [ "A research implementation for rapid prediction of electron-ionization mass spectra from molecular structures using neural-network models." ], "not_designed_for": [ "Electrospray tandem-MS prediction or de novo structure determination from an experimental EI spectrum." ], "requirements": [ "Compatible Python/PyTorch and compiled dependencies, model/meta files and molecular SMILES/InChI inputs; assess the documented model atom limits and CPU/GPU environment." ], "considerations": [ "FormulaNet and SubsetNet have different molecular-size limits. Older NumPy/compiled-library compatibility matters; predicted spectra and library-search results remain model-dependent hypotheses." ] }, "publications": [ { "doi": "10.1021/acs.analchem.2c02093", "type": "primary", "citation": "Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization–Mass Spectrometry. Analytical Chemistry (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "rawhummus", "name": "RawHummus", "summary": "Inspect LC-MS instrument logs and raw QC measurements through an R Shiny interface.", "description": "RawHummus summarizes chromatographic and MS-level quality measurements to help inspect system performance. It produces interactive QC reports and supports local execution; detecting a problem does not automatically correct the underlying data.", "links": { "primary": "https://github.com/YonghuiDong/RawHummus", "repository": "https://github.com/YonghuiDong/RawHummus", "documentation": "https://github.com/YonghuiDong/RawHummus" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package", "web_app" ], "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "capabilities": [ "interactive_dashboard" ] }, "access": { "model": "open_source", "license": "GPL-3.0", "notes": "License checked against official package metadata or repository. Local setup requires the documented dependencies; no installation or hosted-service availability test was performed." }, "data": { "input_types": [ "spectra" ], "output_types": [ "report" ], "other_input_formats": [ "Supported raw QC measurements and instrument log files" ], "other_output_formats": [ "Interactive HTML quality reports" ] }, "common_uses": [ "Inspect run-to-run variation before downstream interpretation." ], "scope": { "designed_for": [ "Inspect LC-MS instrument logs and raw QC measurements through an R Shiny interface." ], "not_designed_for": [ "Confirming compound identity or replacing study design and scientific review." ], "requirements": [ "Representative QC measurements and an R/Shiny environment for local use.", "Supported raw QC measurements and instrument log files; consult the app instructions for instrument-specific import" ], "considerations": [ "The documented free hosted deployment has memory limits; local execution is recommended for larger sets.", "Thresholds are diagnostic aids, not universal acceptance criteria." ] }, "publications": [ { "doi": "10.1093/bioinformatics/btac040", "type": "primary", "citation": "RawHummus: an R Shiny app for automated raw data quality control in metabolomics. Bioinformatics (2022)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate verified against official documentation, source/package metadata and primary-publication DOI/title/year on 2026-10-02. Editorial review only; no software execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "reactome", "name": "Reactome", "summary": "Explore curated metabolic and signaling reactions with literature-linked pathway context.", "description": "Reactome organizes molecules and reactions into pathways through expert curation and review. It offers interactive maps, downloads and programmatic services.", "links": { "primary": "https://reactome.org/what-is-reactome", "documentation": "https://reactome.org/what-is-reactome" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Open-source, open-access resource; data and software license details are available on the site." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "pathway_data", "compound_list" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Molecular identifiers, pathways or species" ], "other_output_formats": [ "Pathway maps and downloadable reaction data" ] }, "publications": [ { "doi": "10.1093/nar/gkaf1223", "citation": "Ragueneau E, Gong C, Sinquin P, et al. The Reactome Knowledgebase 2026. Nucleic Acids Res 54, D673-D681 (2026).", "type": "primary" } ], "resource_citations": [ { "citation": "Reactome team. What is Reactome? Official database, evidence model and access documentation.", "type": "documentation", "url": "https://reactome.org/what-is-reactome" } ], "common_uses": [ "Place annotated metabolites in reaction context.", "Trace pathway assertions to supporting literature." ], "scope": { "designed_for": [ "Place annotated metabolites in reaction context." ], "not_designed_for": [ "Measuring pathway activity or metabolic flux from a database diagram." ], "requirements": [ "Recognized molecular identifiers and an appropriate species context." ], "considerations": [ "Check whether a pathway is directly curated or computationally inferred and preserve database-release information." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "wikipathways", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "redu", "name": "Pan-ReDU", "acronym": "Pan-ReDU", "summary": "A pan-repository metadata harmonization and search infrastructure for finding, contextualizing, downloading, and reanalyzing public metabolomics data across major repositories.", "description": "Pan-ReDU is the current expansion of ReDU for harmonizing controlled sample metadata and indexing public mass-spectrometry files across repositories including MetaboLights, Metabolomics Workbench/NMDR, and GNPS/MassIVE. It links harmonized metadata with persistent run identifiers, raw-file access, and indexed MS/MS searching so researchers can discover, download, co-analyze, and contextualize public metabolomics data across repository boundaries.", "links": { "primary": "https://redu.gnps2.org/", "web_app": "https://redu.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/ReDU_overview/" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "data_management_sharing", "secondary": [ "reference_data_search" ], "capabilities": [ "metadata_contextualization", "repository_scale_search" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "metabolite_profiling", "multiomics" ], "interfaces": [ "web_app", "api" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "technical": { "operating_systems": [ "browser" ], "api_available": true }, "data": { "input_types": [ "sample_metadata" ], "output_types": [ "sample_metadata", "report" ], "input_formats": [ "csv_tsv" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1038/s41592-020-0916-7", "citation": "Jarmusch AK, Wang M, Aceves CM, et al. ReDU: a framework to find and reanalyze public mass spectrometry data. Nat Methods 17, 901-904 (2020).", "type": "primary" }, { "doi": "10.1038/s41467-025-60067-y", "citation": "El Abiead Y, Strobel M, Payne T, et al. Enabling pan-repository reanalysis for big data science of public metabolomics data. Nat Commun 16, 4838 (2025).", "type": "primary" } ], "common_uses": [ "Find public metabolomics files by harmonized organism, biospecimen, disease, phenotype, and other controlled metadata.", "Discover and download files across MetaboLights, Metabolomics Workbench/NMDR, and GNPS/MassIVE using a common metadata layer.", "Contextualize MASST/FASST spectral matches across repositories and prepare public datasets for reanalysis or co-analysis.", "Assemble a public-data cohort using sample metadata, then retrieve files for a documented reanalysis." ], "scope": { "designed_for": [ "Pan-repository discovery, contextualization, and reuse of public mass-spectrometry data using harmonized metadata and file identifiers." ], "requirements": [ "Repository records need sufficiently structured sample information to support the desired query." ], "considerations": [ "Metadata completeness and harmonization quality remain dependent on the information available in the source repositories and historical submissions.", "Repository coverage and indexed file counts evolve as Pan-ReDU is updated.", "Save the selected file identifiers and harmonized metadata with the analysis; harmonization does not make experimental strategies equivalent." ], "not_designed_for": [ "Replacing primary repository records or correcting every missing or inconsistent sample annotation." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "unspecified" ], "ion_mobility": "unspecified" }, "related_tools": [ { "slug": "gnps", "relationship": "integration" }, { "slug": "masst", "relationship": "integration" }, { "slug": "massive", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-21", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "aliases": [ "ReDU", "Reanalysis of Data User Interface" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "refmet", "name": "RefMet", "summary": "Map metabolite names to a standardized reference nomenclature for cross-study comparisons.", "description": "RefMet includes both discrete chemical structures and less-resolved analytical species, including lipid sum compositions, to preserve the level of information reported.", "links": { "primary": "https://www.metabolomicsworkbench.org/databases/refmet/index.php", "documentation": "https://www.metabolomicsworkbench.org/databases/refmet/index.php" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public Metabolomics Workbench service; follow its use and citation guidance." }, "data": { "input_types": [ "compound_list" ], "output_types": [ "compound_list" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Metabolite names or name lists" ], "other_output_formats": [ "Standardized names and mapping metadata" ] }, "publications": [ { "citation": "Fahy E, Subramaniam S. RefMet: a reference nomenclature for metabolomics. Nat Methods 17, 1173-1174 (2020).", "type": "primary", "doi": "10.1038/s41592-020-01009-y" } ], "common_uses": [ "Reconcile names across metabolomics tables.", "Standardize lipid names without adding unsupported structural detail." ], "scope": { "designed_for": [ "Reconcile names across metabolomics tables." ], "not_designed_for": [ "Converting an ambiguous analytical species into a uniquely confirmed structure." ], "requirements": [ "A metabolite name or list of names; unresolved mappings need manual review." ], "considerations": [ "Keep original labels and mapping outcomes. Some names are unmapped or can refer to different structural levels." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metabolomics-workbench", "relationship": "complementary" }, { "slug": "lipid-maps", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "retention-time-gnn", "name": "Retention Time GNN (Kwon et al.)", "summary": "A research implementation of transfer-learning graph neural networks for chromatographic retention-time prediction from molecular structure using small target training datasets.", "links": { "primary": "https://github.com/seokhokang/retention_time_gnn", "repository": "https://github.com/seokhokang/retention_time_gnn", "documentation": "https://github.com/seokhokang/retention_time_gnn#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "machine_learning", "secondary": [ "annotation_identification" ], "capabilities": [ "retention_time_prediction" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "access": { "model": "source_available", "notes": "Public research code; no explicit repository-wide license was identified. Dataset and model availability must be checked separately before reuse." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "Prepared molecular-structure and measured retention-time datasets" ], "other_output_formats": [ "Trained/transfer models, predicted retention times and evaluation results" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2023-11-29", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "This research implementation pretrains a graph neural network and adapts it for retention-time prediction with a smaller target dataset. It provides preprocessing, training and transfer/evaluation scripts rather than a turnkey chromatography-independent predictor.", "scope": { "designed_for": [ "A research implementation of transfer-learning graph neural networks for chromatographic retention-time prediction from molecular structure using small target training datasets." ], "not_designed_for": [ "A universal retention-time scale, raw chromatographic alignment or compound identification based only on predicted time." ], "requirements": [ "Python, PyTorch, DGL and RDKit with appropriately prepared molecular structures and retention-time training/target data; configure the selected target dataset before transfer learning." ], "considerations": [ "Retention depends on the chromatographic method. Avoid structural or sample leakage between training and test sets, and assess applicability to the target LC conditions." ] }, "common_uses": [ "A research implementation of transfer-learning graph neural networks for chromatographic retention-time prediction from molecular structure using small target training datasets." ], "publications": [ { "doi": "10.1021/acs.analchem.3c03177", "type": "primary", "citation": "Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network. Analytical Chemistry (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "rhermes", "name": "RHermes", "summary": "An R package for broad-scoped targeted and semi-targeted LC-MS metabolomics analysis designed to deeply characterize biological and environmental samples.", "links": { "primary": "https://github.com/RogerGinBer/RHermes", "repository": "https://github.com/RogerGinBer/RHermes", "documentation": "https://rogerginber.github.io/RHermes/articles/RHermes_UserGuide.html" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "data_acquisition", "spectral_analysis" ], "capabilities": [ "chromatogram_building", "peak_detection" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "targeted", "semi_targeted" ], "interfaces": [ "r_package", "desktop_gui" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "raw_instrument_data", "compound_list" ], "output_types": [ "feature_table", "compound_list" ], "other_input_formats": [ "mzML LC-MS data and R molecular-formula/adduct databases" ], "other_output_formats": [ "R signal/annotation objects, inclusion lists and interactive plots" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-05-22", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "RHermes implements the HERMES formula-oriented workflow for LC-MS characterization and targeted follow-up. It finds formula/adduct signals in MS1 data, identifies signals of interest and generates acquisition inclusion lists for subsequent MS2 analysis.", "scope": { "designed_for": [ "An R package for broad-scoped targeted and semi-targeted LC-MS metabolomics analysis designed to deeply characterize biological and environmental samples." ], "not_designed_for": [ "Confirming exact structures from an MS1 formula match or automatically executing the follow-up experiment." ], "requirements": [ "R/Bioconductor dependencies, centroided compatible LC-MS data, a formula/adduct database and suitable processing parameters; inclusion-list follow-up requires access to a compatible MS acquisition method." ], "considerations": [ "Formula-database selection constrains coverage and ambiguity. Inspect signals and MS2 evidence; distinguish inclusion-list generation from quantitative calibration or complete structural identification." ] }, "common_uses": [ "An R package for broad-scoped targeted and semi-targeted LC-MS metabolomics analysis designed to deeply characterize biological and environmental samples." ], "publications": [ { "doi": "10.1038/s41592-021-01307-z", "type": "primary", "citation": "HERMES: a molecular-formula-oriented method to target the metabolome. Nature Methods (2021)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ridims", "name": "rIDIMS", "summary": "rIDIMS processes direct-infusion MS by selecting usable scans, forming consensus spectra and filtering features with replicate and background information.", "links": { "primary": "https://github.com/BioinovarLab/rIDIMS", "repository": "https://github.com/BioinovarLab/rIDIMS", "documentation": "https://bioinovarlab.github.io/rIDIMS/articles/rIDIMS.html" }, "resource_types": [ "programming_package", "software_application" ], "functions": { "primary": "preprocessing", "secondary": [ "quality_control", "visualization_reporting" ], "capabilities": [ "blank_filtering", "interactive_dashboard", "reference_spectrum_selection" ] }, "platforms": [ "direct_infusion_ms" ], "interfaces": [ "r_package", "web_app" ], "access": { "model": "open_source", "license": "GPL-3.0-or-later", "notes": "GPL-3.0-or-later source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform", "browser" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra" ], "output_types": [ "spectra", "feature_table" ], "other_input_formats": [ "Direct-infusion mzML files and sample-info XLSX metadata" ], "other_output_formats": [ "Processed/intermediate CSV tables, HTML report and text logs" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-11-07", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "rIDIMS processes direct-infusion MS by selecting usable scans, forming consensus spectra and filtering features with replicate and background information. Its R/Shiny workflow exports intermediate tables and reports for reviewing processing choices.", "scope": { "designed_for": [ "rIDIMS processes direct-infusion MS by selecting usable scans, forming consensus spectra and filtering features with replicate and background information." ], "not_designed_for": [ "Treating scan subsets from one injection as independent biological or experimental replicates." ], "requirements": [ "R with documented dependencies, mzML files and a completed sample-information spreadsheet identifying classes, blanks and replicate groups." ], "considerations": [ "In-silico scan groups represent technical subdivisions of the same sample. Inspect scan selection, thresholds and blank filtering; the published online tutorial includes a package-loading error and is not proof of current operability." ] }, "common_uses": [ "rIDIMS processes direct-infusion MS by selecting usable scans, forming consensus spectra and filtering features with replicate and background information." ], "publications": [ { "doi": "10.1016/j.talanta.2024.127273", "type": "primary", "citation": "rIDIMS: A novel tool for processing direct-infusion mass spectrometry data. Talanta (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "rmsi", "name": "rMSI", "summary": "Open-source R package and GUI for memory-efficient handling, exploration and visualization of mass spectrometry imaging datasets.", "links": { "primary": "https://github.com/prafols/rMSI", "documentation": "https://github.com/prafols/rMSI", "repository": "https://github.com/prafols/rMSI" }, "resource_types": [ "programming_package", "software_application" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "interactive_dashboard" ] }, "platforms": [ "imaging_ms" ], "interfaces": [ "r_package", "desktop_gui" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "R source installation requires compatible native dependencies. The documented GUI uses RGtk2; verify installation on the target operating system before planning an analysis." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official repository README, DESCRIPTION and direct publication metadata reviewed on 2026-09-10. No installation or independent benchmark performed. Ongoing maintenance status has not been established." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "analysis_types": [ "spatial_metabolomics" ], "data": { "input_types": [ "imaging_data" ], "other_output_formats": [ "R imaging objects and interactive ion-image views" ], "output_types": [ "imaging_data", "report" ], "input_formats": [ "imzml" ], "other_input_formats": [ "rMSI TAR format" ] }, "publications": [ { "citation": "Ràfols P, Torres S, Ramírez N, et al. rMSI: an R package for MS imaging data handling and visualization. Bioinformatics 33, 2427–2428 (2017).", "type": "primary", "doi": "10.1093/bioinformatics/btx182", "pmid": "28369250" } ], "related_tools": [ { "slug": "rmsiproc", "relationship": "complementary", "note": "rMSIproc adds a complete preprocessing pipeline." } ], "maintenance": { "status": "unclear", "last_checked": "2026-09-10" }, "description": "rMSI provides disk-backed access to imaging spectra and a graphical interface for ion-image exploration. Its data model supports viewing selected pixels without loading every spectrum into memory.", "common_uses": [ "Inspect ion distributions and selected pixel spectra.", "Access imaging data from R with disk-backed storage." ], "scope": { "designed_for": [ "Imaging-data handling and interactive visualization." ], "not_designed_for": [ "Definitive molecular identification from ion images." ], "requirements": [ "Compatible R, RGtk2 and native dependencies; Windows instructions require Rtools.", "The documented imzML reader supports continuous-mode data; check compatibility before conversion." ], "considerations": [ "Disk-backed storage still needs sufficient local disk space.", "Legacy GUI dependencies may require a carefully configured environment." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "rmsiproc", "name": "rMSIproc", "summary": "Open-source R/C++ package for large-scale MSI preprocessing, including smoothing, alignment, recalibration, normalization, peak picking and binning.", "links": { "primary": "https://github.com/prafols/rMSIproc", "documentation": "https://github.com/prafols/rMSIproc", "repository": "https://github.com/prafols/rMSIproc" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "capabilities": [ "peak_detection" ] }, "platforms": [ "imaging_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "R source installation requires compatible native dependencies. The documented GUI uses RGtk2; verify installation on the target operating system before planning an analysis." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official repository README, DESCRIPTION and direct publication metadata reviewed on 2026-09-10. No installation or independent benchmark performed. Ongoing maintenance status has not been established." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-10", "updated_at": "2026-09-10", "last_verified": "2026-09-10", "last_editorial_review": "2026-09-10" }, "analysis_types": [ "spatial_metabolomics" ], "data": { "input_types": [ "imaging_data" ], "other_output_formats": [ "Processed rMSI TAR datasets and rMSIproc peak-matrix files" ], "output_types": [ "imaging_data", "feature_table" ], "input_formats": [ "imzml" ], "other_input_formats": [ "rMSI TAR format" ] }, "publications": [ { "citation": "Ràfols P, Heijs B, Del Castillo E, et al. rMSIproc: an R package for mass spectrometry imaging data processing. Bioinformatics 36, 3618–3619 (2020).", "type": "primary", "doi": "10.1093/bioinformatics/btaa142", "pmid": "32108859" } ], "related_tools": [ { "slug": "rmsi", "relationship": "complementary", "note": "Designed to complement rMSI data handling and visualization." } ], "maintenance": { "status": "unclear", "last_checked": "2026-09-10" }, "description": "rMSIproc adds C++ processing routines to the rMSI data model, producing processed images and peak matrices through R functions or a parameter wizard.", "common_uses": [ "Extract peak matrices from imaging datasets.", "Apply smoothing, mass alignment and calibration before spatial analysis." ], "scope": { "designed_for": [ "MSI preprocessing with rMSI-compatible data." ], "not_designed_for": [ "Chromatographic retention-time alignment or definitive compound identification." ], "requirements": [ "Working rMSI installation and compatible R/C++ toolchain.", "FFTW support; follow platform-specific installation instructions." ], "considerations": [ "Check mass calibration and binning settings against the instrument resolution.", "Keep preprocessing parameters with output matrices for reproducibility." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "roasmi", "name": "ROASMI", "summary": "Predict relative LC retention behavior and combine it with MS/MS scores to rank candidate structures.", "description": "ROASMI learns molecular representations and pairwise elution preferences using a neural ranking model. Its supplied scripts predict retention scores, optionally retrain models, and integrate retention evidence with MS/MS candidate scores.", "links": { "primary": "https://github.com/FangYuan717/ROASMI", "repository": "https://github.com/FangYuan717/ROASMI", "documentation": "https://github.com/FangYuan717/ROASMI" }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package", "command_line" ], "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "structure_elucidation" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "MIT license verified in the official repository. Software was not installed or execution-tested; dependencies and external services have their own requirements." }, "data": { "input_types": [ "compound_list", "feature_table" ], "output_types": [ "compound_list" ], "input_formats": [ "csv_tsv" ], "other_output_formats": [ "Predicted retention scores and candidate annotation rankings" ] }, "common_uses": [ "Add chromatographic evidence to molecular candidate ranking.", "Retrain a retention-order model with a suitable reference set." ], "scope": { "designed_for": [ "Predict relative LC retention behavior and combine it with MS/MS scores to rank candidate structures." ], "not_designed_for": [ "Replacing experimental confirmation of compound identity.", "Raw-data feature detection or chromatographic alignment." ], "requirements": [ "Python/Conda and the documented dependencies, model files and candidate structures.", "Prepare input CSV files using the repository prediction and identification examples. Identification requires the expected retention and candidate-score information.", "Select reference data and models compatible with the chromatographic mode and buffer pH." ], "considerations": [ "The supplied initial models target reversed-phase LC around pH 2.7; they are not universal retention models.", "The repository supplies retraining examples, including HILIC. That does not establish general applicability of the initial RPLC models to HILIC.", "CPU execution is documented; GPU acceleration is recommended for faster retraining, not required for all use.", "Retention scores describe relative order, not necessarily calibrated absolute retention times." ] }, "publications": [ { "doi": "10.1186/s13321-025-00968-8", "type": "primary", "citation": "ROASMI: accelerating small molecule identification by repurposing retention data. Journal of Cheminformatics (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake reviewed against the primary publication, official repository documentation and license on 2026-10-05. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-05" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-05", "updated_at": "2026-10-05", "last_verified": "2026-10-05", "last_editorial_review": "2026-10-05" }, "related_tools": [ { "slug": "lc-ms2struct", "relationship": "alternative", "note": "Both combine retention-order and spectral evidence; they use different models and are not interchangeable implementations." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "ropls", "name": "ropls", "summary": "Fit and assess multivariate PCA, PLS and OPLS models for omics data in R.", "description": "ropls provides scores, loadings, component assessment, permutation tests and variable-importance measures for exploratory and supervised analysis.", "links": { "primary": "https://bioconductor.org/packages/ropls/", "documentation": "https://bioconductor.org/packages/ropls/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "statistics", "capabilities": [ "multivariate_statistics", "feature_selection" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Bioconductor package; installation and licensing details are provided on the package page." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Numeric matrix and optional response vector" ], "other_output_formats": [ "PCA/PLS/OPLS models, statistics and plots" ] }, "publications": [], "resource_citations": [ { "citation": "ropls developers. ropls: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data. Bioconductor package record.", "type": "software", "doi": "10.18129/B9.bioc.ropls" } ], "common_uses": [ "Explore sample patterns with PCA.", "Evaluate supervised models with validation and permutation testing." ], "scope": { "designed_for": [ "Explore sample patterns with PCA." ], "not_designed_for": [ "Proving predictive performance from separation in a training-set scores plot." ], "requirements": [ "A numeric measurement matrix and appropriate responses for supervised models." ], "considerations": [ "Avoid information leakage during preprocessing and feature selection; use independent validation when assessing generalization." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metaboanalyst", "relationship": "complementary" }, { "slug": "workflow4metabolomics", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "scils-lab", "name": "SCiLS Lab", "summary": "Commercial software suite for processing, statistical analysis, segmentation, annotation and 2D/3D visualization of mass spectrometry imaging data.", "links": { "primary": "https://www.bruker.com/en/products-and-solutions/mass-spectrometry/ms-software/scils-lab.html", "documentation": "https://www.bruker.com/en/landingpages/bdal/SCiLS-Lab-Tutorials.html" }, "resource_types": [ "software_application", "instrument_vendor_suite" ], "functions": { "primary": "visualization_reporting", "secondary": [ "preprocessing", "statistics", "annotation_identification" ], "capabilities": [ "interactive_dashboard", "multivariate_statistics" ] }, "platforms": [ "imaging_ms" ], "interfaces": [ "desktop_gui", "vendor_integration" ], "access": { "model": "commercial", "notes": "Commercial Bruker software. Third-party/imzML import and MetaboScape-powered annotation are listed as optional; confirm the purchased edition and modules." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official Bruker product page and tutorials reviewed on 2026-09-11. No single dedicated publication for the current software suite verified; incidental application papers are excluded. No installation, license activation or benchmark performed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "spatial_metabolomics" ], "data": { "input_types": [ "imaging_data" ], "output_types": [ "imaging_data", "statistical_results", "report" ], "input_formats": [ "imzml", "vendor_raw" ], "output_formats": [ "imzml" ], "other_output_formats": [ "Edition-dependent statistical reports and ion-image exports" ] }, "related_tools": [ { "slug": "metaspace", "relationship": "complementary", "note": "SCiLS imzML exports can support downstream spatial annotation; verify export compatibility." }, { "slug": "m2aia", "relationship": "alternative", "note": "Open-source MSI analysis option with a different feature set." } ], "description": "SCiLS Lab provides an interactive environment for spatial segmentation, statistical comparison and visualization of MSI datasets. Import support and molecular annotation depend on licensed options; the separate SCiLS Scope viewer is not the full analysis product.", "common_uses": [ "Explore spatial segmentation and differences among imaging regions.", "Export supported imaging data to imzML for downstream analysis." ], "scope": { "designed_for": [ "Interactive MSI visualization and statistical analysis." ], "not_designed_for": [ "Assuming every licensed edition includes every advertised annotation or import feature." ], "requirements": [ "A compatible 64-bit Windows workstation and license; consult current vendor hardware and module requirements." ], "considerations": [ "Large datasets require substantial memory and storage.", "Image segmentation or colocalization is not sufficient evidence of molecular identity." ] }, "resource_citations": [ { "citation": "Bruker. SCiLS Software product documentation. Accessed 11 September 2026.", "type": "documentation", "url": "https://www.bruker.com/en/products-and-solutions/mass-spectrometry/ms-software/scils-lab.html" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "seismiq", "name": "SEISMiQ", "summary": "Generate candidate molecular structures from tandem spectra and a supplied molecular formula.", "description": "SEISMiQ is a generative structure-elucidation model developed for drug-substance impurities. Its released prediction workflow and CASMI checkpoints also support small-molecule MS/MS candidate generation.", "links": { "primary": "https://github.com/Boehringer-Ingelheim/seismiq", "repository": "https://github.com/Boehringer-Ingelheim/seismiq", "documentation": "https://github.com/Boehringer-Ingelheim/seismiq#prediction" }, "platforms": [ "lc_ms" ], "interfaces": [ "command_line" ], "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "capabilities": [ "structure_elucidation" ] }, "access": { "model": "open_source", "license": "Apache-2.0", "notes": "License verified in the official repository. Code licensing does not establish the terms for every separately downloaded model or dataset." }, "data": { "input_types": [ "spectra", "compound_list" ], "output_types": [ "molecular_structures" ], "other_input_formats": [ "JSON challenges containing an MS/MS peak list and sum formula" ], "other_output_formats": [ "CSV candidate SMILES and model scores" ] }, "common_uses": [ "Generate candidate molecular structures from tandem spectra and a supplied molecular formula." ], "scope": { "designed_for": [ "SEISMiQ is a generative structure-elucidation model developed for drug-substance impurities. Its released prediction workflow and CASMI checkpoints also support small-molecule MS/MS candidate generation." ], "not_designed_for": [ "Unconstrained identification from spectra alone or proof of metabolite identity." ], "requirements": [ "Python/Poetry environment, compatible model checkpoint and tokenizer.", "MS/MS spectrum and molecular formula; sufficient memory or a suitable GPU." ], "considerations": [ "Training and fine-tuning domains affect applicability to biological metabolites; do not infer universal metabolomics performance.", "Optional substructure prompts and adduct assumptions must be appropriate for the sample." ] }, "publications": [ { "doi": "10.1039/d5dd00115c", "type": "primary", "citation": "Enhancing automated drug substance impurity structure elucidation from tandem mass spectra through transfer learning and domain knowledge. Digital Discovery (2025).", "note": "Final peer-reviewed article replaces the differently titled intake preprint." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake candidate checked against official documentation, license information and primary publication on 2026-10-21. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-21" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-21", "updated_at": "2026-10-21", "last_verified": "2026-10-21", "last_editorial_review": "2026-10-21" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "serda", "name": "SERDA", "summary": "SERDA applies denoising-autoencoder normalization to processed metabolomics data.", "links": { "primary": "https://github.com/slfan2013/SERDA", "repository": "https://github.com/slfan2013/SERDA", "documentation": "https://github.com/slfan2013/SERDA#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "normalization", "secondary": [ "machine_learning" ], "capabilities": [ "batch_effect_correction" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "metabolite_profiling" ], "interfaces": [ "command_line" ], "access": { "model": "source_available", "notes": "Public R research runner; no repository-wide license grant was established. R, Python and dependent neural-network packages require a compatible environment and retain their own terms." }, "technical": { "operating_systems": [ "windows" ], "programming_languages": [ "R", "Python" ] }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Example-formatted metabolomics XLSX abundance/metadata workbook" ], "other_output_formats": [ "Normalized abundance data and runner-generated evaluation results" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2022-02-06", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "SERDA applies denoising-autoencoder normalization to processed metabolomics data. Its R runner connects to a Python/Keras environment and uses an example spreadsheet layout for large-scale GC-MS normalization research.", "scope": { "designed_for": [ "SERDA applies denoising-autoencoder normalization to processed metabolomics data." ], "not_designed_for": [ "Isotope-tracing analysis, raw GC-MS deconvolution or guaranteed removal of technical variation without affecting biology." ], "requirements": [ "A compatible R/reticulate/Keras/Python environment and input XLSX formatted according to the supplied example; update the runner's data-file path before execution." ], "considerations": [ "The documented stack is old and needs compatibility checking. Evaluate normalization with appropriate QC and biological contrasts rather than relying only on a cleaner low-dimensional plot." ] }, "common_uses": [ "SERDA applies denoising-autoencoder normalization to processed metabolomics data." ], "publications": [ { "doi": "10.3390/metabo13080944", "type": "primary", "citation": "Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography–Mass Spectrometry. Metabolites (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "sgmns", "name": "SGMNS", "summary": "SGMNS propagates candidate metabolite annotations through a structure-guided background molecular network.", "links": { "primary": "https://github.com/DLUT-datas/SGMNS", "repository": "https://github.com/DLUT-datas/SGMNS", "documentation": "https://github.com/DLUT-datas/SGMNS#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "molecular_networking", "secondary": [ "annotation_identification" ], "capabilities": [ "structure_database_search" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "command_line" ], "access": { "model": "source_available", "notes": "Repository supplies documentation and links to a multi-part SourceForge executable distribution. A repository-wide reuse license was not established; inspect distribution terms before reuse." }, "technical": { "operating_systems": [ "windows" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "feature_table", "spectra" ], "output_types": [ "compound_list", "statistical_results" ], "other_input_formats": [ "Calibrated peak CSV, MGF spectra, seed lists and background database" ], "other_output_formats": [ "Network-propagated candidate metabolite annotations" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2025-05-17", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "SGMNS propagates candidate metabolite annotations through a structure-guided background molecular network. Its documented workflow combines processed LC-HRMS features, tandem spectra and known seed metabolites with a default or custom structure database.", "scope": { "designed_for": [ "SGMNS propagates candidate metabolite annotations through a structure-guided background molecular network." ], "not_designed_for": [ "Raw peak picking or structure confirmation independent of seed accuracy and background-network coverage." ], "requirements": [ "Prepared positive/negative-mode peak CSV tables, MGF spectra, seed-metabolite lists and a configured background network; retention times must be calibrated to the background conditions." ], "considerations": [ "Ion redundancy removal and retention-time calibration are upstream prerequisites. The default network and imported seed errors can propagate through subsequent annotations." ] }, "common_uses": [ "SGMNS propagates candidate metabolite annotations through a structure-guided background molecular network." ], "publications": [ { "doi": "10.1021/acs.analchem.3c00849", "type": "primary", "citation": "A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation. Analytical Chemistry (2023)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "sigmaccs", "name": "SigmaCCS", "summary": "Predict collision cross sections from molecular structures and ion adducts.", "description": "SigmaCCS uses molecular graphs with three-dimensional conformations and adduct information to predict CCS for candidate annotation filtering.", "links": { "primary": "https://github.com/zmzhang/SigmaCCS#readme", "repository": "https://github.com/zmzhang/SigmaCCS", "documentation": "https://github.com/zmzhang/SigmaCCS#readme" }, "platforms": [ "platform_independent" ], "interfaces": [ "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "capabilities": [ "ccs_prediction" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "MIT code license checked. Separately distributed predicted datasets and model resources may carry their own terms; no execution test." }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "report" ], "other_input_formats": [ "SMILES and adduct records plus model parameters and trained weights" ], "other_output_formats": [ "Predicted CCS values for candidate ions" ] }, "common_uses": [ "Predict collision cross sections from molecular structures and ion adducts." ], "scope": { "designed_for": [ "SigmaCCS uses molecular graphs with three-dimensional conformations and adduct information to predict CCS for candidate annotation filtering." ], "not_designed_for": [ "Instrument CCS calibration or confirmation of a molecular identity from CCS alone." ], "requirements": [ "Python, RDKit, TensorFlow and Spektral; molecular conformer generation and the supplied model/parameter files." ], "considerations": [ "The documented large predicted collection covers [M+H]+, [M+Na]+ and [M-H]-; do not assume support for arbitrary adducts.", "Check applicability to the candidate chemistry and measurement conditions before using predicted CCS as a filter." ] }, "publications": [ { "doi": "10.1038/s42004-023-00939-w", "type": "primary", "citation": "Highly accurate and large-scale collision cross sections prediction with graph neural networks. Communications Chemistry (2023)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "simile", "name": "SIMILE", "summary": "A Python library for relating fragmentation spectra using statistically assessed fragment and neutral-loss relationships, including pairwise and multiple spectral matching.", "links": { "primary": "https://github.com/biorack/simile", "repository": "https://github.com/biorack/simile", "documentation": "https://github.com/biorack/simile#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "secondary": [ "annotation_identification" ], "capabilities": [ "ms2_processing" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package" ], "access": { "model": "source_available", "license": "Modified BSD (full terms not located)", "notes": "README and pyproject label the code Modified BSD, but the linked license.txt is absent from the current repository. Exact terms need clarification; no precise SPDX license or unrestricted grant is inferred. Record the implementation version because V2 changes matching/statistics." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "statistical_results" ], "other_input_formats": [ "Python fragment-mass arrays and optional precursor-mass arrays" ], "other_output_formats": [ "Similarity/matching matrices, p-values and matching-ion data frames" ] }, "publications": [ { "doi": "10.1038/s41467-022-30118-9", "type": "primary", "citation": "SIMILE enables alignment of tandem mass spectra with statistical significance. Nature Communications (2022)." } ], "common_uses": [ "Detect significant relationships among MS/MS spectra despite multiple structural differences." ], "maintenance": { "status": "unclear", "repository_last_activity": "2022-07-20", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "SIMILE compares fragmentation spectra through relationships among fragment ions and estimates statistical significance of their alignment. Its Python implementation produces similarity/matching matrices, scores and matching-ion reports for spectral interpretation.", "scope": { "designed_for": [ "A Python library for relating fragmentation spectra using statistically assessed fragment and neutral-loss relationships, including pairwise and multiple spectral matching." ], "not_designed_for": [ "Experimental validation of molecular identity or a general guarantee that a low p-value establishes structural equivalence." ], "requirements": [ "The documented Python environment, arrays of fragment m/z values with optional precursor masses, and appropriate tolerances and significance-testing settings." ], "considerations": [ "Interpret significance in the context of the null model and multiple comparisons. The current V2 matching procedure differs from the original paper, and the supplied environment uses older dependencies." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "simpel", "name": "SIMPEL", "summary": "Extract and correct isotopologue distributions from isotope-enriched metabolomics feature lists.", "description": "SIMPEL is an R package for single- and dual-label HRMS experiments. It combines processed features with compound formula/retention-time information and produces mass-isotopologue distributions and labeling summaries. Flux modelling in the primary paper is performed downstream with separate modelling software.", "links": { "primary": "https://github.com/SIMPELmetabolism/SIMPEL", "repository": "https://github.com/SIMPELmetabolism/SIMPEL", "documentation": "https://github.com/SIMPELmetabolism/SIMPEL" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "programming_package" ], "functions": { "primary": "isotope_tracing_flux", "capabilities": [ "isotopologue_correction" ] }, "access": { "model": "open_source", "license": "GPL-3.0", "notes": "R package with a repository tutorial and declared R dependencies." }, "data": { "input_types": [ "feature_table", "compound_list" ], "output_types": [ "isotope_tracing_data", "report" ], "other_input_formats": [ "Processed feature lists plus compound formulae and retention-time information" ], "other_output_formats": [ "Mass-isotopologue distribution matrices and labeling plots" ] }, "common_uses": [ "Prepare isotope-enrichment measurements for downstream modelling." ], "scope": { "designed_for": [ "Post-processing transient stable-isotope labeling experiments." ], "not_designed_for": [ "A substitute for raw-data preprocessing or a standalone metabolic-network flux solver." ], "requirements": [ "Resolved isotope measurements, compound information and a configured R environment." ], "considerations": [ "Instrument resolving power and tracer design constrain which isotope species can be distinguished." ] }, "publications": [ { "doi": "10.1038/s42003-024-05844-z", "type": "primary", "citation": "SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism. Communications Biology (2024)." } ], "maintenance": { "status": "unclear", "last_checked": "2026-10-02" }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Discovered through the Enveda review intake; official repository and primary publication metadata checked on 2026-10-02. Editorial review only; software and performance not execution-tested." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-02", "updated_at": "2026-10-02", "last_verified": "2026-10-02", "last_editorial_review": "2026-10-02" }, "related_tools": [ { "slug": "xcms", "relationship": "integration", "note": "The primary paper uses XCMS feature lists before SIMPEL." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "sirius", "name": "SIRIUS", "summary": "A cross-platform small-molecule MS/MS framework for LC-MS preprocessing, molecular-formula annotation, spectral-library searching, structure-database searching, compound-class prediction, confidence scoring, and de novo structure generation.", "links": { "primary": "https://v6.docs.sirius-ms.io/", "homepage": "https://www.bright-giant.com/sirius/", "repository": "https://github.com/sirius-ms/sirius", "documentation": "https://v6.docs.sirius-ms.io/" }, "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning", "preprocessing", "reference_data_search", "spectral_analysis" ], "capabilities": [ "chromatogram_building", "compound_class_prediction", "identification_confidence", "library_matching", "molecular_formula_prediction", "ms2_processing", "peak_detection", "retention_time_alignment", "structure_database_search", "structure_elucidation", "substructure_prediction" ] }, "platforms": [ "lc_ms", "direct_infusion_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "desktop_gui", "command_line", "api" ], "access": { "model": "free_academic", "notes": "Local features and webservice-backed features have different access and licensing conditions. The official documentation should be checked for the current academic and commercial terms." }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "programming_languages": [ "Java" ], "api_available": true }, "data": { "input_types": [ "spectra", "feature_table" ], "output_types": [ "compound_list", "molecular_structures", "report" ], "input_formats": [ "mzml", "mgf", "msp", "mzxml" ], "output_formats": [ "csv_tsv", "json" ] }, "publications": [ { "doi": "10.1038/s41592-019-0344-8", "citation": "Dührkop K, Fleischauer M, Ludwig M, et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods 16, 299-302 (2019).", "type": "primary" } ], "common_uses": [ "Import centroided LC-MS mzML/mzXML files and perform feature detection/alignment into a SIRIUS project space.", "Annotate molecular formulas using isotope patterns, fragmentation trees, and optional ZODIAC dataset-level re-ranking.", "Perform identity and analog spectral-library searching, including custom spectral/structure databases.", "Search molecular-structure databases with CSI:FingerID/COSMIC, predict compound classes with CANOPUS, and generate de novo candidates with MSNovelist.", "Export formula, structure and class results while retaining feature identifiers for linking back to the abundance table." ], "related_tools": [ { "slug": "mzmine", "relationship": "integration" }, { "slug": "csi-fingerid", "relationship": "contains", "note": "CSI:FingerID provides molecular-fingerprint prediction and structure-database searching within SIRIUS." }, { "slug": "canopus", "relationship": "contains", "note": "CANOPUS provides compound-class prediction within SIRIUS." }, { "slug": "msnovelist", "relationship": "contains", "note": "MSNovelist provides de novo structure generation within SIRIUS." } ], "maintenance": { "status": "active", "last_checked": "2026-08-24", "latest_version": "6.3.12", "latest_release_date": "2026-07-29" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "Molecular-formula and structure-candidate annotation from accurate-mass MS/MS data, including compound-class prediction when exact structures remain uncertain." ], "not_designed_for": [ "Peptides, polymers, or stereochemical elucidation; SIRIUS is designed primarily for accurate-mass small molecules, usually below about 1000 Da." ], "requirements": [ "High-mass-accuracy MS and preferably informative MS/MS data; the current documentation recommends mass deviations within about 20 ppm.", "A SIRIUS account/license and internet connection are required for webservice-backed features such as CSI:FingerID, CANOPUS, and MSNovelist; these remain free for academic use under current terms." ], "considerations": [ "Formula, library, class, and structure results represent computational evidence and should be combined with orthogonal evidence and appropriate metabolite-identification confidence levels.", "SIRIUS contains both local open-source analysis and proprietary/webservice-backed functions, so access requirements differ among workflow components.", "DIA spectra require prior deconvolution; the documented default workflow expects centroided, high-accuracy spectra with little chimeric signal." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "description": "SIRIUS is a Java-based small-molecule MS/MS framework integrating local and webservice-backed analysis. Current SIRIUS 6 can import centroided mzML/mzXML runs, perform feature detection/alignment with lcms-align, search spectral libraries by identity or analog similarity, annotate molecular formulas with fragmentation trees and isotope patterns, re-rank formulas with ZODIAC, search structure databases through CSI:FingerID/COSMIC, predict compound classes with CANOPUS, and generate de novo candidates with MSNovelist. GUI, CLI, and API-oriented project-space workflows share a common analysis environment." }, { "$schema": "../../schemas/tool.schema.json", "slug": "skyline", "name": "Skyline", "summary": "Develop and review targeted mass-spectrometry assays, including small-molecule quantification.", "description": "Skyline supports chromatogram inspection, transition-based methods, calibration and quantitative reporting. Dedicated tutorials cover metabolomics and lipidomics applications.", "links": { "primary": "https://skyline.ms/home/software/Skyline/wiki-page.view?name=tutorials-small-mol", "documentation": "https://skyline.ms/home/software/Skyline/wiki-page.view?name=tutorials-small-mol" }, "resource_types": [ "software_application" ], "functions": { "primary": "quantification", "capabilities": [ "calibration_curve", "chromatogram_building" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "desktop_gui" ], "access": { "model": "source_available", "notes": "Software source is distributed through ProteoWizard; consult component and vendor-reader terms." }, "data": { "input_types": [ "raw_instrument_data", "compound_list" ], "output_types": [ "feature_table", "report" ], "input_formats": [ "vendor_raw" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1093/bioinformatics/btq054", "citation": "MacLean B, Tomazela DM, Shulman N, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966-968 (2010).", "type": "primary", "note": "Foundational Skyline software paper, originally scoped to targeted proteomics. Consult the linked small-molecule documentation for metabolomics workflows." } ], "resource_citations": [ { "citation": "Skyline team. Small Molecule Quantification and Small Molecules tutorials. Official Skyline documentation.", "type": "documentation", "url": "https://skyline.ms/home/software/Skyline/wiki-page.view?name=tutorials-small-mol" } ], "common_uses": [ "Review targeted small-molecule peak integration.", "Quantify analytes using external calibration and internal standards." ], "scope": { "designed_for": [ "Review targeted small-molecule peak integration." ], "not_designed_for": [ "Unrestricted discovery of all unknown compounds in a raw file." ], "requirements": [ "A compatible installation, assay targets and supported measurement files." ], "considerations": [ "Confirm transition specificity, calibration range and internal-standard behavior before reporting concentrations." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "smartpeak", "relationship": "complementary" }, { "slug": "lipidr", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "slaw", "name": "SLAW", "summary": "A scalable, containerized workflow for untargeted LC-MS preprocessing with automated parameter optimization, feature detection, alignment, gap filling, isotope/adduct grouping, and DDA MS/MS consolidation.", "links": { "primary": "https://github.com/zamboni-lab/SLAW", "repository": "https://github.com/zamboni-lab/SLAW", "documentation": "https://github.com/zamboni-lab/SLAW#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "preprocessing", "secondary": [ "workflow_reproducibility", "quality_control", "spectral_analysis" ], "capabilities": [ "peak_detection", "retention_time_alignment", "gap_filling", "adduct_isotope_grouping", "ms2_processing", "container_support" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "command_line" ], "access": { "model": "open_source", "license": "GPL-2.0", "notes": "GPL-2.0 code with a documented container distribution. Container execution and dependency versions should be recorded for reproducibility." }, "technical": { "operating_systems": [ "linux", "windows", "containers" ], "programming_languages": [ "R", "Python" ], "containers": [ "Docker" ] }, "data": { "input_types": [ "raw_instrument_data", "sample_metadata" ], "output_types": [ "feature_table", "spectra" ], "input_formats": [ "mzml", "csv_tsv" ], "output_formats": [ "csv_tsv", "mgf" ] }, "publications": [ { "doi": "10.1021/acs.analchem.1c02687", "type": "primary", "citation": "SLAW: A Scalable and Self-Optimizing Processing Workflow for Untargeted LC-MS. Analytical Chemistry (2021)." } ], "common_uses": [ "Run reproducible preprocessing of large untargeted LC-MS studies.", "Optimize peak picking, alignment, and gap-filling parameters using QC samples.", "Produce feature tables and consolidated DDA MS/MS spectra for downstream analysis." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-08-03", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "Scalable untargeted LC-MS preprocessing from centroided mzML files." ], "requirements": [ "Docker or a compatible Singularity workflow, centroided single-polarity mzML files and an edited parameters file; representative pooled QCs and an injection-order sample table are recommended." ], "considerations": [ "DIA-MS2 is skipped. If QCs are not specified, the workflow selects samples for optimization; the selected reference material affects optimized parameters." ], "not_designed_for": [ "DIA-MS2 deconvolution, direct processing of profile data or universal processing of mixed-polarity acquisitions." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda" ], "ion_mobility": "unspecified" }, "description": "SLAW is a containerized untargeted LC-MS processing workflow that wraps multiple peak-picking algorithms and optimizes processing parameters. It performs alignment, gap filling, ion grouping and consolidation of DDA tandem spectra for downstream analysis." }, { "$schema": "../../schemas/tool.schema.json", "slug": "smart-formula", "name": "SMART", "summary": "Rank molecular-formula candidates for spatial-metabolomics mass features.", "description": "SMART uses a formula network and regression scoring to prioritize formula assignments from accurate mass and polarity.", "links": { "primary": "https://github.com/bioinfo-ibms-pumc/SMART#readme", "repository": "https://github.com/bioinfo-ibms-pumc/SMART", "documentation": "https://github.com/bioinfo-ibms-pumc/SMART#readme" }, "platforms": [ "imaging_ms" ], "interfaces": [ "desktop_gui", "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "capabilities": [ "molecular_formula_prediction" ] }, "access": { "model": "source_available", "notes": "Public source and release assets are available; no explicit software license was identified. Data availability differs between released assets and the larger raw database." }, "data": { "input_types": [ "feature_table" ], "output_types": [ "compound_list" ], "other_input_formats": [ "Single m/z or table of m/z values, polarity and mass tolerance" ], "other_output_formats": [ "Ranked formula candidates and supporting database evidence" ] }, "common_uses": [ "Rank molecular-formula candidates for spatial-metabolomics mass features." ], "scope": { "designed_for": [ "SMART uses a formula network and regression scoring to prioritize formula assignments from accurate mass and polarity." ], "not_designed_for": [ "Unambiguous structure identification or raw imaging-data extraction." ], "requirements": [ "Python and documented dependencies; matching SMART database and regression model files.", "Release v0.1 provides smart_full.db and lr_with_sc.pkl; assets were listed but not downloaded or executed." ], "considerations": [ "Formula assignments remain hypotheses requiring additional evidence.", "The README describes a larger raw database requiring author contact; do not equate it with the smaller release asset." ] }, "publications": [ { "doi": "10.1021/acs.analchem.4c06210", "type": "primary", "citation": "SMART: An Approach for Accurate Formula Assignment in Spatially Resolved Metabolomics. Analytical Chemistry (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-23. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-23" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-23", "updated_at": "2026-10-23", "last_verified": "2026-10-23", "last_editorial_review": "2026-10-23" }, "analysis_types": [ "spatial_metabolomics" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "smart-metabolomics", "name": "SMART (Statistical Metabolomics Analysis)", "summary": "Analyze metabolomics data with the SMART R/GUI processing, QC and statistical modules.", "links": { "primary": "https://github.com/YuJenL/SMART", "repository": "https://github.com/YuJenL/SMART", "documentation": "https://github.com/YuJenL/SMART#readme" }, "resource_types": [ "software_application" ], "functions": { "primary": "statistics", "secondary": [ "preprocessing", "quality_control", "annotation_identification", "visualization_reporting" ], "capabilities": [ "univariate_statistics", "multivariate_statistics" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "targeted", "untargeted" ], "interfaces": [ "desktop_gui", "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "The current GitHub repository carries GPL-3.0; legacy v1.1 documentation describes different terms. This record describes the current repository and does not extend its terms to older downloads." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "raw_instrument_data", "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "statistical_results", "report" ], "other_input_formats": [ "Module-dependent raw/mzXML files or processed R abundance tables" ], "other_output_formats": [ "R peak/statistical/calibration results and graphical analysis views" ] }, "common_uses": [ "Run integrated metabolomics statistics and visualization from raw or processed data." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-06-03", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "SMART is an R/GUI environment for metabolomics data processing, QC and statistical analysis. Its modules connect data import and peak analysis with transformations, batch exploration, association/classification analysis and optional concentration calibration.", "scope": { "designed_for": [ "An R-based graphical metabolomics analysis suite spanning data import, peak analysis and annotation, preprocessing, quality control, batch-effect assessment, statistics, and visualization." ], "not_designed_for": [ "The unrelated spatial-metabolomics SMART formula-ranking application or automatic justification of every statistical model and vendor format." ], "requirements": [ "The distribution's R/GUI environment, module-compatible raw or processed data and sample/design metadata; calibration and targeted modules additionally require their corresponding reference information." ], "considerations": [ "Import and analysis requirements vary by module and release. Current README citation text is stale about SMART 2.0 publication; cite the verified published update and distinguish legacy distribution terms." ] }, "publications": [ { "doi": "10.1021/acs.analchem.6b00603", "type": "primary", "citation": "SMART: Statistical Metabolomics Analysis—An R Tool. Analytical Chemistry (2016)." }, { "doi": "10.1021/acs.analchem.5c03225", "type": "primary", "citation": "SMART 2.0 Statistical Metabolomics Analysis: An R Tool 2.0. Analytical Chemistry (2025)." } ], "aliases": [ "SMART 2.0", "Statistical Metabolomics Analysis" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "smartpeak", "name": "SmartPeak", "summary": "Automate targeted and semi-targeted chromatographic data processing and quantification.", "description": "Built on OpenMS, SmartPeak combines peak detection, integration, calibration and QC reporting with graphical and command-line interfaces.", "links": { "primary": "https://github.com/AutoFlowResearch/SmartPeak", "documentation": "https://github.com/AutoFlowResearch/SmartPeak", "repository": "https://github.com/AutoFlowResearch/SmartPeak" }, "resource_types": [ "software_application" ], "functions": { "primary": "quantification", "capabilities": [ "calibration_curve", "peak_detection" ] }, "platforms": [ "lc_ms", "gc_ms", "ce_ms" ], "interfaces": [ "desktop_gui", "command_line" ], "access": { "model": "open_source", "notes": "MIT-licensed application; underlying components have their own licenses." }, "data": { "input_types": [ "spectra", "sample_metadata" ], "output_types": [ "feature_table", "report" ], "input_formats": [ "mzml", "csv_tsv" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "citation": "Kutuzova S, Colaianni P, Röst H, et al. SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing. Anal Chem 92, 15968-15974 (2020).", "type": "primary", "doi": "10.1021/acs.analchem.0c03421" } ], "common_uses": [ "Process targeted LC-MS batches.", "Review calibration curves and quantitative QC reports." ], "scope": { "designed_for": [ "Process targeted LC-MS batches." ], "not_designed_for": [ "Obtaining defensible concentrations without suitable calibration and assay validation." ], "requirements": [ "Compatible measurement files, a sequence table and method-specific configuration." ], "considerations": [ "Inspect integration and calibration fits, especially near detection limits or in the presence of interference." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "openms", "relationship": "complementary" }, { "slug": "skyline", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "smqvp", "name": "SMQVP", "summary": "Inspect and filter spatial-metabolomics peak matrices through interactive QC views.", "description": "SMQVP is a Shiny application for assessing background, noise, intensity, missingness and isotope/adduct patterns in spatial peak tables.", "links": { "primary": "https://github.com/mzlab-research/SMQVP", "repository": "https://github.com/mzlab-research/SMQVP", "documentation": "https://github.com/mzlab-research/SMQVP#readme" }, "platforms": [ "imaging_ms" ], "interfaces": [ "web_app" ], "resource_types": [ "software_application" ], "functions": { "primary": "quality_control", "capabilities": [ "interactive_dashboard", "adduct_isotope_grouping" ] }, "access": { "model": "source_available", "notes": "Source is public and an online service is documented; an explicit software license was not identified. No hosted data upload or service execution test performed." }, "data": { "input_types": [ "imaging_data" ], "output_types": [ "imaging_data", "report" ], "other_input_formats": [ "Peak matrix with X/Y pixel coordinates followed by m/z intensity columns" ], "other_output_formats": [ "Processed spatial peak matrix and QC visualizations" ] }, "common_uses": [ "Inspect and filter spatial-metabolomics peak matrices through interactive QC views." ], "scope": { "designed_for": [ "SMQVP is a Shiny application for assessing background, noise, intensity, missingness and isotope/adduct patterns in spatial peak tables." ], "not_designed_for": [ "Raw imaging-file conversion or definitive molecular identification." ], "requirements": [ "An extracted spatial peak table and appropriate tissue/background region selection.", "R and Shiny for local use, or the documented hosted interface." ], "considerations": [ "Review filtering against tissue/background signals; QC images alone do not validate biological conclusions." ] }, "publications": [ { "doi": "10.3390/metabo15060354", "type": "primary", "citation": "SMQVP: A Web Application for Spatial Metabolomics Quality Visualization and Processing. Metabolites (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "analysis_types": [ "spatial_metabolomics" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "spamtp", "name": "SpaMTP", "summary": "Open-source framework for joint analysis, annotation, alignment, integration and visualization of spatial metabolomics and spatial transcriptomics.", "links": { "primary": "https://genomicsmachinelearning.github.io/SpaMTP/", "repository": "https://github.com/GenomicsMachineLearning/SpaMTP", "documentation": "https://genomicsmachinelearning.github.io/SpaMTP/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "multiomics_integration", "secondary": [ "annotation_identification", "statistics", "visualization_reporting", "pathway_interpretation" ], "capabilities": [ "univariate_statistics", "multivariate_statistics", "pathway_enrichment", "container_support" ] }, "platforms": [ "imaging_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "Open-source R package with documented installation and an Apptainer environment. Stable and developmental branches have separate documentation; experimental features should not be assumed part of a reproducible stable workflow." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official package documentation, repository license and Nature Methods publication verified on 2026-09-11. Stable and developmental functionality distinguished. No installation, statistical analysis or benchmark performed. RaMP-DB relationship remains queued until that entry is added." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-08", "updated_at": "2026-09-12", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" }, "analysis_types": [ "spatial_metabolomics", "multiomics" ], "data": { "input_types": [ "imaging_data", "feature_table", "sample_metadata" ], "output_types": [ "statistical_results", "pathway_data", "report" ], "other_input_formats": [ "Spatial-omics data imported into supported R objects", "Seurat-compatible expression and coordinate data" ], "other_output_formats": [ "R analysis objects, statistical tables and visualizations" ] }, "publications": [ { "citation": "Causer A, Lu T, Kriel J, et al. SpaMTP: integrative statistical analysis and visualization of spatial metabolomics and transcriptomics data. Nature Methods 23, 1501–1506 (2026).", "type": "primary", "doi": "10.1038/s41592-026-03140-8" } ], "related_tools": [ { "slug": "cardinal", "relationship": "dependency", "note": "The project builds on Cardinal MSI functionality." }, { "slug": "metaspace", "relationship": "complementary", "note": "An alternative source of spatial annotation evidence; direct interchange was not tested." }, { "slug": "ramp-db", "relationship": "integration", "note": "Provides pathway context used by SpaMTP; inspect identifier mapping and reference versions." } ], "description": "SpaMTP combines spatial metabolomics and transcriptomics analysis in an R environment built around Seurat objects and Cardinal functionality. It supports m/z-based candidate annotation, statistical comparison and pathway analysis, alongside visualization and multimodal integration.", "common_uses": [ "Compare spatial metabolite patterns and associated pathways.", "Integrate spatial MS measurements with transcriptomic context." ], "scope": { "designed_for": [ "Joint spatial metabolomics and transcriptomics analysis." ], "not_designed_for": [ "Treating m/z-only candidate annotation as definitive identification." ], "requirements": [ "R and documented package dependencies, or the supplied container environment.", "Appropriate spatial coordinates and compatible inputs for the chosen workflow." ], "considerations": [ "Annotation ambiguity propagates to pathway interpretation.", "Alignment and statistical comparisons depend on sampling geometry and biological replication.", "Record the branch, package version and reference databases used." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "spec2vec", "name": "Spec2Vec", "summary": "Compare tandem mass spectra using embeddings learned from recurring peaks and neutral losses.", "description": "Spec2Vec adapts Word2Vec to spectral data, representing spectra through learned relationships between fragment patterns rather than only direct peak overlap.", "links": { "primary": "https://spec2vec.readthedocs.io/en/latest/", "documentation": "https://spec2vec.readthedocs.io/en/latest/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "library_matching" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "notes": "Open-source software; use the official installation, license and citation guidance for the selected release." }, "data": { "input_types": [ "spectra" ], "output_types": [ "statistical_results" ], "input_formats": [ "mgf" ], "output_formats": [ "other" ], "other_output_formats": [ "Spectral embeddings and similarity scores" ] }, "publications": [ { "citation": "Huber F, Ridder L, Verhoeven S, et al. Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships. PLoS Comput Biol 17, e1008724 (2021).", "type": "primary", "doi": "10.1371/journal.pcbi.1008724" } ], "common_uses": [ "Rank related spectra for exploratory analysis.", "Compare embedding-based and conventional spectral similarity." ], "scope": { "designed_for": [ "Rank related spectra for exploratory analysis." ], "not_designed_for": [ "Assigning a unique structure from the embedding score alone." ], "requirements": [ "Python, processed spectra and a pretrained or locally trained Word2Vec model." ], "considerations": [ "Model coverage matters: peaks absent from training can weaken a score. Record the model and missing-peak threshold." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "matchms", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "specreboot", "name": "SpecReBoot", "aliases": [], "summary": "SpecReBoot brings the statistical rigor of phylogenetic bootstrapping to mass spectrometry. It resamples spectral features to estimate edge support and build consensus molecular networks with quantified confidence.", "links": { "primary": "https://github.com/ECharria/SpecReBoot", "repository": "https://github.com/ECharria/SpecReBoot" }, "resource_types": [ "programming_package" ], "functions": { "primary": "molecular_networking", "secondary": [ "spectral_analysis", "reference_data_search" ], "capabilities": [] }, "platforms": [ "platform_independent" ], "analysis_types": [], "interfaces": [ "python_package" ], "access": { "model": "open_source" }, "publications": [ { "doi": "10.64898/2026.02.03.703446", "type": "primary" } ], "common_uses": [], "scope": {}, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-24" }, "credits": [], "provenance": { "submitted_by": "curator", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/63", "developer_verified": false, "submitter_name": "Morgane Mauduit", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4583-5389", "notes": "Status: in active development \nFeedback, ideas, issues, and PRs are very welcome!" }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-24", "updated_at": "2026-09-24", "last_verified": "2026-09-24" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "spectra", "name": "Spectra", "summary": "Store, filter, process and compare mass spectra through a common R interface.", "description": "Spectra separates analysis methods from storage backends, supporting both in-memory processing and approaches suited to larger collections.", "links": { "primary": "https://bioconductor.org/packages/Spectra/", "documentation": "https://bioconductor.org/packages/Spectra/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "capabilities": [ "ms2_processing" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Open-source software; use the official installation, license and citation guidance for the selected release." }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Spectra objects and backend-supported files" ], "other_output_formats": [ "Spectra objects and backend-supported exports" ] }, "publications": [], "resource_citations": [ { "citation": "Spectra developers. Spectra: Spectra Infrastructure for Mass Spectrometry Data. Bioconductor package record.", "type": "software", "doi": "10.18129/B9.bioc.Spectra" } ], "common_uses": [ "Build reproducible spectral processing code.", "Use compatible backends to work with large spectral datasets." ], "scope": { "designed_for": [ "Build reproducible spectral processing code." ], "not_designed_for": [ "Automatic compound identification without matching methods and reference data." ], "requirements": [ "R/Bioconductor and a storage backend appropriate for the input data." ], "considerations": [ "Backend capabilities differ; confirm which operations and export routes the selected backend supports." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metaboannotation", "relationship": "complementary" }, { "slug": "msnbase", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "spectral-denoising", "name": "spectral-denoising", "summary": "Spectral Denoising removes candidate electronic and chemical noise ions from tandem spectra.", "links": { "primary": "https://github.com/FanzhouKong/spectral_denoising", "repository": "https://github.com/FanzhouKong/spectral_denoising", "documentation": "https://spectral-denoising.readthedocs.io/en/latest/index.html" }, "resource_types": [ "programming_package" ], "functions": { "primary": "spectral_analysis", "secondary": [ "annotation_identification" ], "capabilities": [ "ms2_processing", "library_matching" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ], "package_managers": [ "PyPI" ] }, "data": { "input_types": [ "spectra", "spectral_library" ], "output_types": [ "spectra", "compound_list" ], "other_input_formats": [ "MSP spectra or peak arrays with formula/SMILES and adduct information" ], "other_output_formats": [ "Denoised peak arrays and candidate library-match result tables" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2024-11-21", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "Spectral Denoising removes candidate electronic and chemical noise ions from tandem spectra. Denoising Search applies candidate-dependent molecular information during reference-library matching and returns comparisons for raw and denoised spectra.", "scope": { "designed_for": [ "Spectral Denoising removes candidate electronic and chemical noise ions from tandem spectra." ], "not_designed_for": [ "Formula-independent removal of all interference or proof of a compound identity from an improved matching score alone." ], "requirements": [ "Python 3.8-3.12 with RDKit and dependencies; chemical denoising needs a molecular formula or SMILES plus adduct information, and search needs a compatible reference library." ], "considerations": [ "Candidate chemistry and precursor tolerances influence which ions are retained. Validate thresholds and preserve original spectra for assessing possible removal of genuine fragments." ] }, "common_uses": [ "Spectral Denoising removes candidate electronic and chemical noise ions from tandem spectra." ], "publications": [ { "doi": "10.1038/s41592-025-02646-x", "type": "primary", "citation": "Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer. Nature Methods (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "spectripy", "name": "SpectriPy", "summary": "An R package that connects the RforMassSpectrometry Spectra ecosystem with Python, enabling exchange and joint analysis of mass-spectrometry data across R and Python workflows.", "links": { "primary": "https://github.com/rformassspectrometry/SpectriPy", "repository": "https://github.com/rformassspectrometry/SpectriPy", "documentation": "https://rformassspectrometry.github.io/SpectriPy/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_conversion", "secondary": [ "spectral_analysis", "workflow_reproducibility" ], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "Artistic-2.0", "notes": "Artistic-2.0 R package; Python dependencies have their own licenses and version constraints. SpectriPy wraps their functionality rather than replacing the underlying methods." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "R", "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "spectra" ], "other_input_formats": [ "R Spectra, Python matchms Spectrum and spectrum_utils objects" ], "other_output_formats": [ "Converted spectral objects and similarity/filter results returned to R" ] }, "publications": [ { "doi": "10.21105/joss.08070", "type": "primary", "citation": "SpectriPy: Enhancing Cross-Language Mass Spectrometry Data Analysis with R and Python. Journal of Open Source Software (2025)." } ], "common_uses": [ "Exchange spectral data between R Spectra objects and Python-based analysis code." ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-05-12", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "SpectriPy connects the R Spectra ecosystem to Python MS libraries through reticulate. It translates spectral objects and exposes Python similarity, filtering and normalization functions inside R workflows.", "scope": { "designed_for": [ "An R package that connects the RforMassSpectrometry Spectra ecosystem with Python, enabling exchange and joint analysis of mass-spectrometry data across R and Python workflows." ], "not_designed_for": [ "A standalone raw-data preprocessing engine or a guarantee that different similarity implementations produce interchangeable scores." ], "requirements": [ "R/Bioconductor Spectra, reticulate and a compatible Python environment with matchms, spectrum_utils and NumPy. Follow the documented automatic or manual environment setup route." ], "considerations": [ "Python environment selection and library versions matter. Data conversion and score parameters should be recorded when mixing language ecosystems." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "spectus", "name": "SpecTUS", "summary": "SpecTUS translates GC-electron-ionization mass spectra into candidate SMILES structures using a transformer pretrained on synthetic spectra and fine-tuned on experimental spectra.", "links": { "primary": "https://github.com/hejjack/SpecTUS", "repository": "https://github.com/hejjack/SpecTUS", "documentation": "https://github.com/hejjack/SpecTUS#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [ "machine_learning" ], "capabilities": [ "structure_elucidation" ] }, "platforms": [ "gc_ms" ], "interfaces": [ "notebook", "command_line" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source code. The final NIST20-trained model is withheld because of proprietary training data; public synthetic data/pretraining checkpoints and hosted demo have separate terms and availability." }, "technical": { "operating_systems": [ "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra" ], "output_types": [ "molecular_structures" ], "other_input_formats": [ "EI mass spectra, including documented MSP inference inputs" ], "other_output_formats": [ "Ranked candidate molecular structures as SMILES" ] }, "publications": [ { "doi": "10.1021/acs.analchem.6c02423", "type": "primary", "citation": "SpecTUS: Spectral Translator for Unknown Structures Annotation from EI-MS Spectra. Analytical Chemistry (2026)." } ], "maintenance": { "status": "unclear", "repository_last_activity": "2026-09-15", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "SpecTUS translates GC-electron-ionization mass spectra into candidate SMILES structures using a transformer pretrained on synthetic spectra and fine-tuned on experimental spectra. The repository provides research code, synthetic resources and a pretraining checkpoint.", "scope": { "designed_for": [ "SpecTUS translates GC-electron-ionization mass spectra into candidate SMILES structures using a transformer pretrained on synthetic spectra and fine-tuned on experimental spectra." ], "not_designed_for": [ "ESI tandem-spectrum identification or assured identification of unknowns from a generated candidate alone." ], "requirements": [ "The documented conda environment and trained checkpoint; the final NIST20-trained model is not distributed, so reproducing it requires licensed NIST20 data and training." ], "considerations": [ "Public pretraining resources and a hosted demonstration are distinct from access to the final trained model. Assess candidate ranking and domain coverage against suitable reference data." ] }, "common_uses": [ "SpecTUS translates GC-electron-ionization mass spectra into candidate SMILES structures using a transformer pretrained on synthetic spectra and fine-tuned on experimental spectra." ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "specxplore", "name": "specXplore", "summary": "An open-source Python resource for interactive exploration and visualization of mass-spectral similarity relationships.", "links": { "primary": "https://github.com/kevinmildau/specXplore", "repository": "https://github.com/kevinmildau/specXplore", "documentation": "https://github.com/kevinmildau/specXplore#readme" }, "resource_types": [ "programming_package" ], "functions": { "primary": "visualization_reporting", "secondary": [ "spectral_analysis", "molecular_networking" ], "capabilities": [ "network_visualization", "interactive_dashboard" ] }, "platforms": [ "platform_independent" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package", "notebook" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. Local preprocessing may download separate similarity models and libraries; compatibility must follow the repository rather than a generic cross-platform claim." }, "technical": { "operating_systems": [ "linux", "macos" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra", "statistical_results" ], "output_types": [ "report" ], "other_input_formats": [ "MGF spectra with feature_id metadata, or a preprocessed session object" ], "other_output_formats": [ "Saved session objects and interactive dashboard/network views" ] }, "common_uses": [ "Explore spectral-similarity neighborhoods and relationships interactively." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-03-21", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "specXplore combines a spectral-similarity embedding with interactive network, heatmap and fragment views. A notebook preprocessing stage creates a saved session object that is opened in the dashboard for local and global exploration.", "scope": { "designed_for": [ "An open-source Python resource for interactive exploration and visualization of mass-spectral similarity relationships." ], "not_designed_for": [ "Confirming structures from t-SNE proximity or acting as a raw chromatographic peak detector." ], "requirements": [ "MGF MS/MS data with feature_id metadata, a compatible Python/Jupyter environment and appropriate similarity-model/library assets for the enabled importing pipeline." ], "considerations": [ "The README contains conflicting Windows guidance and warns about affected macOS ARM ms2deepscore predictions. Check the documented compatibility issue before trusting default similarity calculations." ] }, "publications": [ { "doi": "10.1021/acs.analchem.3c04444", "type": "primary", "citation": "Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard. Analytical Chemistry (2024)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "stattarget", "name": "statTarget", "summary": "Correct signal drift and analyze molecular-profile data from single- or multi-batch studies.", "description": "statTarget provides QC-based random-forest signal correction, batch handling and statistical analysis, with graphical workflow options.", "links": { "primary": "https://bioconductor.org/packages/statTarget/", "documentation": "https://bioconductor.org/packages/statTarget/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "quality_control", "capabilities": [ "qc_drift_correction", "batch_effect_correction" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "LGPL-3-or-later package; see the package license and vignettes." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Abundance table, QC and injection metadata" ], "other_output_formats": [ "Corrected abundance tables and statistics" ] }, "publications": [ { "doi": "10.1016/j.aca.2018.08.002", "citation": "Luan H, Ji F, Chen Y, et al. statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data. Anal Chim Acta 1036, 66-72 (2018).", "type": "primary" } ], "resource_citations": [ { "citation": "statTarget developers. statTarget: Statistical Analysis of Molecular Profiles. Bioconductor package record.", "type": "software", "doi": "10.18129/B9.bioc.statTarget" } ], "common_uses": [ "Evaluate analytical drift across injections.", "Prepare corrected metabolomics tables for statistical comparisons." ], "scope": { "designed_for": [ "Evaluate analytical drift across injections." ], "not_designed_for": [ "Recovering a biological effect that is completely confounded with acquisition batch." ], "requirements": [ "Abundance and sample metadata, including injection order and appropriate QC information for QC-based correction." ], "considerations": [ "Inspect corrected QC behavior and retained biological variation; correction assumptions differ between methods." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "noreva", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "structuremasst", "name": "StructureMASST", "summary": "A GNPS2 web application for structure- and substructure-centric searching across public metabolomics repositories using chemical names, SMILES, SMARTS, and matched reference spectra.", "description": "StructureMASST changes the starting point of repository-scale metabolomics search from one expert-selected MS/MS spectrum to a chemical structure or substructure. It retrieves relevant reference spectra, performs multi-spectrum MASST searches, and uses precomputed FASST Records with harmonized metadata to map molecules and structural families across public repositories, biological contexts, taxonomy, and environmental conditions. Searches can include exact structures, substructures, and modification-tolerant relationships.", "links": { "primary": "https://structure-masst.gnps2.org/", "web_app": "https://structure-masst.gnps2.org/", "repository": "https://github.com/Wang-Bioinformatics-Lab/Structure_MASST_App", "documentation": "https://github.com/Wang-Bioinformatics-Lab/Structure_MASST_App" }, "resource_types": [ "web_service", "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "secondary": [ "annotation_identification", "data_management_sharing" ], "capabilities": [ "repository_scale_search", "metadata_contextualization", "structure_database_search", "substructure_prediction" ] }, "platforms": [ "multi_platform" ], "analysis_types": [ "untargeted", "metabolite_profiling", "exposomics" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Hosted access and any account or usage requirements should be checked at the linked service. Access to a web service does not establish the license of its code or reference data." }, "publications": [ { "doi": "10.1038/s41587-026-03082-8", "citation": "El Abiead Y, Seo JI, Charron-Lamoureux V, et al. Structure-centric searching enables global mapping of the public metabolome. Nat Biotechnol (2026).", "type": "primary" } ], "common_uses": [ "Search public metabolomics data starting from a chemical name, SMILES, or SMARTS representation.", "Map a structure or substructure across multiple reference spectra and public datasets rather than relying on one query spectrum.", "Explore exact, substructure, and modification-tolerant distributions across harmonized biological and environmental metadata.", "Map a chemical family to public datasets and examine the metadata of individual matches." ], "scope": { "designed_for": [ "Structure-centric and substructure-centric exploration of public MS/MS repositories." ], "requirements": [ "A chemical name or structure representation that can be mapped to reference spectra for repository searching." ], "considerations": [ "Mass-spectral matching generally does not resolve stereochemistry, and coverage is limited by available reference spectra and indexed public data.", "StructureMASST uses computational spectral matches as evidence of occurrence; important findings should be validated with appropriate orthogonal evidence.", "Inspect the reference spectra behind a structure query and distinguish exact-structure, substructure and analog matches." ], "not_designed_for": [ "Searching all theoretically possible structures without corresponding reference spectral evidence." ] }, "acquisition": { "ms_levels": [ "ms2" ], "strategies": [ "dda", "targeted_msms", "unspecified" ], "ion_mobility": "not_required" }, "related_tools": [ { "slug": "gnps", "relationship": "part_of" }, { "slug": "fasst", "relationship": "uses" }, { "slug": "masst", "relationship": "complementary" }, { "slug": "redu", "relationship": "integration" } ], "maintenance": { "status": "active", "last_checked": "2026-08-24" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-24", "updated_at": "2026-09-08", "last_verified": "2026-08-24", "last_editorial_review": "2026-09-06" }, "technical": { "operating_systems": [ "browser" ], "api_available": false }, "data": { "input_types": [ "molecular_structures" ], "output_types": [ "spectra", "sample_metadata", "report" ], "input_formats": [ "smiles_inchi" ], "output_formats": [ "csv_tsv" ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "tardis", "name": "TARDIS", "summary": "An R package for targeted raw-data integration in mass spectrometry, designed to extract and quantify user-specified signals directly from raw or converted data.", "links": { "primary": "https://github.com/UGent-LIMET/TARDIS", "repository": "https://github.com/UGent-LIMET/TARDIS", "documentation": "https://pablovgd.github.io/TARDIS/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "quantification", "secondary": [ "preprocessing" ], "capabilities": [ "chromatogram_building" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "targeted" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "GPL-3.0", "notes": "GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "windows", "macos", "linux" ], "programming_languages": [ "R" ] }, "data": { "input_types": [ "raw_instrument_data", "compound_list" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Centroided mzML or MsExperiment plus CSV/Excel/R target tables" ], "other_output_formats": [ "Peak-area/intensity/quality tables and diagnostic EIC plots" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-10-27", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "TARDIS integrates chromatographic peaks for user-specified LC-MS targets and reports peak-quality metrics. It uses target masses/retention times, optional QC/internal-standard context and xcms-based retention correction through R and a GUI.", "scope": { "designed_for": [ "An R package for targeted raw-data integration in mass spectrometry, designed to extract and quantify user-specified signals directly from raw or converted data." ], "not_designed_for": [ "Untargeted discovery of every chromatographic feature or absolute concentration determination without a suitable calibration procedure." ], "requirements": [ "R/Bioconductor dependencies, centroided mzML or an appropriate MsExperiment object, and a target table with identifiers, m/z, expected RT and polarity; configure QC and batch positions when used." ], "considerations": [ "Inspect EICs and target windows before full integration. Multiple overlapping scan windows must be analyzed separately as documented to avoid sawtooth chromatograms." ] }, "common_uses": [ "An R package for targeted raw-data integration in mass spectrometry, designed to extract and quantify user-specified signals directly from raw or converted data." ], "publications": [ { "doi": "10.1021/acs.analchem.5c00567", "type": "primary", "citation": "Automated Integration and Quality Assessment of Chromatographic Peaks in LC–MS-Based Metabolomics and Lipidomics Using TARDIS. Analytical Chemistry (2025)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "tidymass", "name": "TidyMass", "summary": "Connect LC-MS processing, quality control, annotation and interpretation through an R package ecosystem.", "description": "TidyMass uses shared objects and conventions to connect analysis stages while retaining data and processing context. Individual modules can also be used separately.", "links": { "primary": "https://www.tidymass.org/", "documentation": "https://www.tidymass.org/" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "capabilities": [ "provenance_tracking" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Open R ecosystem; inspect component and reference-data licenses." }, "data": { "input_types": [ "spectra", "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "compound_list", "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "LC-MS files or TidyMass data objects" ], "other_output_formats": [ "TidyMass analysis objects and reports" ] }, "publications": [ { "citation": "Shen X, Yan H, Wang C, et al. TidyMass an object-oriented reproducible analysis framework for LC-MS data. Nat Commun 13, 4365 (2022).", "type": "primary", "doi": "10.1038/s41467-022-32155-w" } ], "common_uses": [ "Build a consistent workflow from LC-MS data to interpretation.", "Track processing across coordinated R packages." ], "scope": { "designed_for": [ "Build a consistent workflow from LC-MS data to interpretation." ], "not_designed_for": [ "Eliminating the need to evaluate preprocessing choices and annotation evidence." ], "requirements": [ "R, compatible TidyMass package versions and module-specific data resources." ], "considerations": [ "The ecosystem includes older and newer package documentation; keep a consistent set of versions and record database snapshots." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "metid", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "tidyms", "name": "TidyMS", "summary": "A Python library for reading, processing, curating, and visualizing mass-spectrometry data in metabolomics, including LC-MS feature detection, correspondence, and data-matrix curation.", "links": { "primary": "https://github.com/griquelme/tidyms", "repository": "https://github.com/griquelme/tidyms", "documentation": "https://tidyms.readthedocs.io/en/latest/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [ "quality_control", "visualization_reporting" ], "capabilities": [ "chromatogram_building", "peak_detection", "retention_time_alignment" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "BSD-3-Clause", "notes": "BSD-3-Clause Python library with documentation and example notebooks. It is an API for composing workflows, so users must select and record processing steps." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "raw_instrument_data", "feature_table" ], "output_types": [ "feature_table", "report" ], "other_input_formats": [ "mzML files or supported processed tables imported into TidyMS objects" ], "other_output_formats": [ "Python feature/data-matrix objects and plotting objects" ] }, "common_uses": [ "Detect and align LC-MS features.", "Curate metabolomics data matrices and inspect raw or processed data." ], "maintenance": { "status": "unclear", "repository_last_activity": "2024-07-13", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "TidyMS provides Python building blocks for reading, processing and visualizing metabolomics MS data. It supports chromatogram/spectrum construction, peak detection, LC-MS feature correspondence and curation of processed data matrices.", "scope": { "designed_for": [ "A Python library for reading, processing, curating, and visualizing mass-spectrometry data in metabolomics, including LC-MS feature detection, correspondence, and data-matrix curation." ], "not_designed_for": [ "A turnkey structure-identification service or automatic applicability of one peak-picking configuration to every acquisition." ], "requirements": [ "A Python environment with TidyMS dependencies, mzML raw data or supported processed feature tables, and analysis-specific parameters chosen through the documented API/notebook examples." ], "considerations": [ "Upstream conversion, peak detection and data-matrix filtering influence results. Validate correspondence and filtering decisions on representative samples." ] }, "publications": [ { "doi": "10.3390/metabo10100416", "type": "primary", "citation": "A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows. Metabolites (2020)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "timar", "name": "timaR", "aliases": [], "summary": "timaR helps annotate metabolites in MS/MS data by combining spectral matching with taxonomic information about the organisms being studied. It can incorporate retention-time evidence and existing SIRIUS or GNPS annotations to prioritize candidate compounds.", "links": { "primary": "https://taxonomicallyinformedannotation.github.io/tima/", "homepage": "https://taxonomicallyinformedannotation.github.io/tima/", "documentation": "https://taxonomicallyinformedannotation.github.io/tima/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "annotation_identification", "secondary": [], "capabilities": [ "library_matching" ] }, "platforms": [ "lc_ms" ], "analysis_types": [], "interfaces": [ "r_package" ], "access": { "model": "open_source", "notes": "Install through the documented R-universe repository; the documentation also describes a local Shiny interface and a Docker distribution. Consult the current setup instructions and terms for external resources." }, "publications": [ { "doi": "10.3389/fpls.2019.01329", "type": "primary", "citation": "Rutz A, Dounoue-Kubo M, Ollivier S, et al. Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation. Frontiers in Plant Science 10:1329 (2019).", "note": "Foundational method cited by the official tima documentation; it does not describe every later R-package update." } ], "common_uses": [ "Prioritize candidate annotations using organism taxonomy alongside MS/MS evidence.", "Combine compatible SIRIUS or GNPS annotations with a processed feature dataset." ], "scope": { "designed_for": [ "Downstream annotation of processed LC-MS/MS features with organism context." ], "not_designed_for": [ "Replacing raw-data feature extraction or confirming chemical identity from taxonomy alone." ], "requirements": [ "Feature table with feature IDs, retention time, m/z and sample intensities; MGF spectra; sample-to-organism metadata when multiple organisms are analyzed." ], "considerations": [ "Taxonomic context and reference-library coverage affect candidate prioritization.", "External annotations and custom libraries are optional inputs; check documented version and column conventions." ] }, "acquisition": {}, "related_tools": [], "maintenance": { "status": "unclear", "last_checked": "2026-09-14" }, "credits": [], "provenance": { "submitted_by": "user", "source_issue": "https://github.com/scottjarmusch/metabolomics-navigator/issues/28", "developer_verified": false, "submitter_name": "Elnur Garayev", "public_attribution_consent": true, "submitter_orcid": "0000-0003-4790-4878", "notes": "Draft editorial additions based on the official tima documentation. Original submitter attribution is retained. Software execution was not tested." }, "status": { "entry": "stub", "review": "unreviewed", "created_at": "2026-09-14", "updated_at": "2026-09-15", "last_verified": "2026-09-14" }, "description": "An R package for Taxonomically Informed Metabolite Annotation. The workflow combines processed feature information and fragmentation spectra with organism metadata and structure-organism evidence. External annotations and custom libraries can supplement the workflow; resulting candidates require interpretation rather than being treated as confirmed identities.", "data": { "input_types": [ "feature_table", "spectra", "sample_metadata" ], "output_types": [ "compound_list", "report" ], "input_formats": [ "csv_tsv", "mgf" ], "output_formats": [ "other" ], "other_output_formats": [ "Workflow files in .tima/data; exact tables depend on the selected workflow." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "timsconvert", "name": "TIMSCONVERT", "summary": "An open-source workflow for converting Bruker timsTOF raw data, including trapped-ion-mobility information, into open mzML and imzML formats for downstream analysis.", "links": { "primary": "https://github.com/gtluu/timsconvert", "repository": "https://github.com/gtluu/timsconvert", "documentation": "https://gtluu.github.io/timsconvert/" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "data_conversion", "secondary": [], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "command_line", "desktop_gui" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 workflow code includes components covered by separate Bruker proprietary terms. The required TDF-SDK route is not compatible with macOS according to the project documentation." }, "technical": { "operating_systems": [ "windows", "linux" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "raw_instrument_data" ], "output_types": [ "spectra", "imaging_data" ], "input_formats": [ "vendor_raw" ], "output_formats": [ "mzml", "imzml" ] }, "publications": [ { "doi": "10.1093/bioinformatics/btac419", "type": "primary", "citation": "TIMSCONVERT: a workflow to convert trapped ion mobility data to open data formats. Bioinformatics (2022)." } ], "common_uses": [ "Convert timsTOF data to open formats while preserving ion-mobility information." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-03-14", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "unspecified" ], "ion_mobility": "required" }, "description": "TIMSCONVERT converts supported Bruker timsTOF raw acquisitions to open mzML or imzML while retaining ion-mobility information where supported. It provides graphical and command-line routes to data conversion for downstream analysis.", "scope": { "designed_for": [ "An open-source workflow for converting Bruker timsTOF raw data, including trapped-ion-mobility information, into open mzML and imzML formats for downstream analysis." ], "not_designed_for": [ "Feature extraction, metabolite identification or conversion of arbitrary proprietary vendor formats." ], "requirements": [ "Supported timsTOF acquisition directories and the documented Windows/Linux environment with the required Bruker TDF-SDK components; output mode must match LC-MS or imaging acquisition." ], "considerations": [ "Conversion options and mobility handling must suit the acquisition and downstream reader. Preserve original raw data and record conversion parameters." ] } }, { "$schema": "../../schemas/tool.schema.json", "slug": "tinymass", "name": "TinyMass", "summary": "Hosted utility for sharing mass spectra through reusable spectrum identifiers in the GNPS2 ecosystem.", "description": "TinyMass accepts a peak list and an optional precursor m/z through a web form. The GNPS2 documentation describes it as a way to share a spectrum and obtain a Universal Spectrum Identifier for use by other tools.", "links": { "primary": "https://tinymass.gnps2.org/", "documentation": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/toolindex/#tinymass-app" }, "resource_types": [ "web_service" ], "functions": { "primary": "visualization_reporting", "capabilities": [ "provenance_tracking" ] }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Public hosted submission interface. No software license or private-storage guarantee is inferred from access." }, "data": { "input_types": [ "spectra" ], "other_input_formats": [ "Pasted peak list and optional precursor m/z" ], "output_types": [ "report" ], "other_output_formats": [ "Shareable spectrum identifier (USI)" ] }, "common_uses": [ "Share individual spectra for discussion or downstream visualization.", "Reference a spectrum in compatible GNPS2 tools." ], "scope": { "designed_for": [ "Lightweight sharing of individual mass spectra." ], "not_designed_for": [ "Private data storage", "full experimental-data deposition or molecular identification." ], "requirements": [ "Internet access and a peak list suitable for the submission form." ], "considerations": [ "Treat submitted spectra as shared data. The GNPS2 library search interface states that peak lists deposited through its TinyMass integration are retained indefinitely.", "Keep original files and acquisition metadata; a shared spectrum alone does not capture experimental provenance.", "Confirm identifier retrieval and downstream compatibility before relying on it in a published workflow." ] }, "related_tools": [ { "slug": "gnps", "relationship": "part_of", "note": "Listed as a GNPS2 spectrum-sharing utility." }, { "slug": "gnps-usi-playground", "relationship": "complementary", "note": "USI Playground constructs identifiers for existing dataset or task spectra; TinyMass accepts a peak list for sharing." } ], "publications": [], "resource_citations": [ { "citation": "GNPS2 Documentation. TinyMass App. Accessed 11 September 2026.", "type": "documentation", "url": "https://wang-bioinformatics-lab.github.io/GNPS2_Documentation/toolindex/#tinymass-app" }, { "citation": "GNPS2 Library Knowledgebase. Spectrum search retention notice. Accessed 11 September 2026.", "type": "documentation", "url": "https://library.gnps2.org/" } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Official tool index, public TinyMass form labels and GNPS2 library retention notice reviewed on 2026-09-11. No spectrum was submitted. No standalone TinyMass publication verified; the 2016 GNPS paper displayed by the app remains on the GNPS parent entry." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-11", "updated_at": "2026-09-11", "last_verified": "2026-09-11", "last_editorial_review": "2026-09-11" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "umetaflow", "name": "UmetaFlow", "summary": "A Snakemake workflow for high-throughput untargeted metabolomics that combines file conversion, OpenMS preprocessing, SIRIUS-family annotation, GNPS export, spectral matching, and graph-based outputs.", "links": { "primary": "https://github.com/biosustain/snakemake_UmetaFlow", "repository": "https://github.com/biosustain/snakemake_UmetaFlow", "documentation": "https://github.com/biosustain/snakemake_UmetaFlow#readme" }, "resource_types": [ "workflow_pipeline" ], "functions": { "primary": "workflow_reproducibility", "secondary": [ "data_conversion", "preprocessing", "annotation_identification", "spectral_analysis", "molecular_networking" ], "capabilities": [ "provenance_tracking", "container_support", "peak_detection", "retention_time_alignment", "gap_filling", "library_matching", "molecular_formula_prediction", "compound_class_prediction" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "command_line", "workflow_language" ], "access": { "model": "open_source", "license": "Apache-2.0", "notes": "Apache-2.0 workflow code. External programs, databases and hosted annotation services retain separate licenses and access requirements." }, "technical": { "operating_systems": [ "linux", "macos" ], "programming_languages": [ "Python", "Snakemake" ], "package_managers": [ "Conda" ] }, "data": { "input_types": [ "raw_instrument_data", "spectra", "sample_metadata" ], "output_types": [ "feature_table", "spectra", "compound_list" ], "input_formats": [ "vendor_raw", "mzml" ], "output_formats": [ "csv_tsv", "mgf" ] }, "publications": [ { "doi": "10.1186/s13321-023-00724-w", "type": "primary", "citation": "UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis. Journal of Cheminformatics (2023)." } ], "common_uses": [ "Execute a reproducible untargeted LC-MS pipeline around OpenMS.", "Prepare outputs for SIRIUS-family annotation and GNPS feature-based molecular networking." ], "maintenance": { "status": "unclear", "repository_last_activity": "2025-01-05", "last_checked": "2026-10-27", "notes": "Repository state checked; recent commits alone do not establish ongoing maintenance or operational compatibility." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official README/documentation, repository access terms and primary publication metadata on 2026-10-27. Editorial source review only; no software execution or independent benchmarking. See docs/enveda-expansion-review-2026-10-27.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "scope": { "designed_for": [ "High-throughput untargeted metabolomics workflows combining preprocessing and downstream annotation/export." ], "requirements": [ "A documented Linux/macOS conda and Snakemake environment, acquisition-appropriate configuration and sample tables; enable only annotation modules whose external software, models and libraries are available." ], "considerations": [ "Thermo raw conversion is integrated; other vendor files generally require independent mzML conversion. Module-dependent candidate annotations need inspection beyond workflow completion." ], "not_designed_for": [ "A guarantee of structural identification or identical processing for every vendor's proprietary raw format." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "unspecified" ], "ion_mobility": "unspecified" }, "description": "UmetaFlow organizes untargeted metabolomics processing with Snakemake and OpenMS/pyOpenMS. It combines feature detection, alignment and optional re-quantification with configurable spectral matching, formula/class prediction and GNPS-compatible exports." }, { "$schema": "../../schemas/tool.schema.json", "slug": "vimms", "name": "ViMMS", "summary": "ViMMS simulates LC-MS/MS acquisition at scan level so researchers can prototype and compare fragmentation strategies.", "links": { "primary": "https://github.com/glasgowcompbio/vimms", "repository": "https://github.com/glasgowcompbio/vimms", "documentation": "https://glasgowcompbio.github.io/vimms/" }, "resource_types": [ "programming_package" ], "functions": { "primary": "data_acquisition", "secondary": [ "workflow_reproducibility" ], "capabilities": [ "spectral_data_io" ] }, "platforms": [ "lc_ms" ], "analysis_types": [ "untargeted" ], "interfaces": [ "python_package" ], "access": { "model": "open_source", "license": "MIT", "notes": "MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested." }, "technical": { "operating_systems": [ "cross_platform" ], "programming_languages": [ "Python" ] }, "data": { "input_types": [ "spectra", "feature_table" ], "output_types": [ "raw_instrument_data", "spectra" ], "other_input_formats": [ "Empirical LC-MS data or configured virtual chemicals/controllers" ], "other_output_formats": [ "Simulated acquisition results and mzML scans" ] }, "maintenance": { "status": "unclear", "repository_last_activity": "2026-07-03", "last_checked": "2026-10-27", "notes": "Repository state checked; commit recency is not operational validation or proof of ongoing support." }, "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-27", "updated_at": "2026-10-27", "last_verified": "2026-10-27", "last_editorial_review": "2026-10-27" }, "description": "ViMMS simulates LC-MS/MS acquisition at scan level so researchers can prototype and compare fragmentation strategies. It can generate virtual acquisitions from empirical or simulated chemical data and export simulated scans for downstream processing.", "scope": { "designed_for": [ "ViMMS simulates LC-MS/MS acquisition at scan level so researchers can prototype and compare fragmentation strategies." ], "not_designed_for": [ "Proof that a strategy's simulated performance will reproduce on every physical mass spectrometer." ], "requirements": [ "The documented Python environment, empirical data or configured virtual chemicals and an acquisition controller; real-instrument experiments additionally require a compatible instrument API." ], "considerations": [ "Chemical models, scan timing and instrument assumptions constrain simulation realism. Validate promising strategies on the intended acquisition platform." ] }, "common_uses": [ "ViMMS simulates LC-MS/MS acquisition at scan level so researchers can prototype and compare fragmentation strategies." ], "publications": [ { "doi": "10.3390/metabo9100219", "type": "primary", "citation": "In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics. Metabolites (2019)." }, { "doi": "10.21105/joss.03990", "type": "primary", "citation": "ViMMS 2.0: A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics. Journal of Open Source Software (2022)." } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "vinsmoc", "name": "VInSMoC", "summary": "Search tandem spectra against candidate molecules and their mass-shifted variants.", "description": "VInSMoC scores spectrum–molecule matches using exact or variable search and provides statistical assessment of matches. Variable mode considers modifications consistent with precursor-mass differences.", "links": { "primary": "https://github.com/mohimanilab/VInSMoC#readme", "repository": "https://github.com/mohimanilab/VInSMoC", "documentation": "https://github.com/mohimanilab/VInSMoC#readme" }, "platforms": [ "lc_ms" ], "interfaces": [ "web_app", "command_line" ], "resource_types": [ "software_application" ], "functions": { "primary": "annotation_identification", "capabilities": [ "structure_elucidation", "identification_confidence" ] }, "access": { "model": "source_available", "license": "CC-BY-NC-SA-4.0", "notes": "Repository license restricts reuse to noncommercial purposes with share-alike conditions. The documented hosted workflow requires an account; service operation was not tested." }, "data": { "input_types": [ "spectra", "molecular_structures" ], "output_types": [ "compound_list", "report" ], "other_input_formats": [ "MGF, mzML or mzXML spectra and a candidate-molecule database" ], "other_output_formats": [ "Scored spectrum–molecule hits; variable-mode candidate modifications" ] }, "common_uses": [ "Search tandem spectra against candidate molecules and their mass-shifted variants." ], "scope": { "designed_for": [ "VInSMoC scores spectrum–molecule matches using exact or variable search and provides statistical assessment of matches. Variable mode considers modifications consistent with precursor-mass differences." ], "not_designed_for": [ "Reference-free de novo structure determination or definitive identification from a score alone." ], "requirements": [ "Select candidate database, polarity, charge and precursor/product mass tolerances.", "Hosted account or local Rust build with the dependencies specified in code/README.md." ], "considerations": [ "Candidate coverage and fragmentation settings constrain the search; examine statistical evidence and competing candidates.", "Web and local implementations have different setup requirements. No installation or benchmark was performed." ] }, "publications": [ { "doi": "10.1038/s43588-025-00923-5", "type": "primary", "citation": "Identifying variants of molecules through database search of mass spectra. Nature Computational Science (2025)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-23. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-23" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-23", "updated_at": "2026-10-23", "last_verified": "2026-10-23", "last_editorial_review": "2026-10-23" } }, { "$schema": "../../schemas/tool.schema.json", "slug": "waveica", "name": "WaveICA 2.0", "summary": "Correct injection-order-associated variation in untargeted metabolomics tables.", "description": "WaveICA 2.0 uses wavelet decomposition and independent-component analysis to adjust inter- and intra-batch effects without requiring batch labels.", "links": { "primary": "https://github.com/dengkuistat/WaveICA_2.0/blob/master/man/WaveICA_2.0.Rd", "repository": "https://github.com/dengkuistat/WaveICA_2.0", "documentation": "https://github.com/dengkuistat/WaveICA_2.0/blob/master/man/WaveICA_2.0.Rd" }, "platforms": [ "lc_ms" ], "interfaces": [ "r_package" ], "resource_types": [ "software_application" ], "functions": { "primary": "normalization", "capabilities": [ "batch_effect_correction" ] }, "access": { "model": "open_source", "license": "MIT", "notes": "Repository LICENSE grants MIT terms; DESCRIPTION inconsistently says License: No. Check the license file when reusing code. Not execution-tested." }, "data": { "input_types": [ "feature_table", "sample_metadata" ], "output_types": [ "feature_table" ], "other_input_formats": [ "Sample-by-peak numeric matrix and corresponding injection-order vector" ], "other_output_formats": [ "R result list containing corrected data" ] }, "common_uses": [ "Correct injection-order-associated variation in untargeted metabolomics tables." ], "scope": { "designed_for": [ "WaveICA 2.0 uses wavelet decomposition and independent-component analysis to adjust inter- and intra-batch effects without requiring batch labels." ], "not_designed_for": [ "Raw-file peak detection or recovery of biology irretrievably confounded with acquisition order." ], "requirements": [ "R with waveslim, ica, mgcv and the package dependencies.", "Reliable injection order aligned to each matrix row." ], "considerations": [ "Review correction against QC samples and biological covariates; removal of order-associated variation can also remove confounded biological effects.", "Batch labels are not required, but injection-order metadata is required." ] }, "publications": [ { "doi": "10.1007/s11306-021-01839-7", "type": "primary", "citation": "WaveICA 2.0: a novel batch effect removal method for untargeted metabolomics data without using batch information. Metabolomics (2021)." } ], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Enveda intake checked against official documentation, license information and primary DOI/title/year on 2026-10-22. Editorial review only; no execution or independent benchmark." }, "maintenance": { "status": "unclear", "last_checked": "2026-10-22" }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-10-22", "updated_at": "2026-10-22", "last_verified": "2026-10-22", "last_editorial_review": "2026-10-22" }, "analysis_types": [ "untargeted" ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "wikipathways", "name": "WikiPathways", "summary": "Find and contribute biological pathway diagrams and machine-readable pathway knowledge.", "description": "WikiPathways supports community curation, review and versioned releases. Its pathway records can connect metabolite measurements with other molecular data.", "links": { "primary": "https://www.wikipathways.org/", "documentation": "https://github.com/wikipathways/wikipathways-database", "repository": "https://github.com/wikipathways/wikipathways-database" }, "resource_types": [ "database_knowledgebase" ], "functions": { "primary": "reference_data_search", "capabilities": [ "structure_database_search" ] }, "platforms": [ "platform_independent" ], "interfaces": [ "web_app" ], "access": { "model": "free_web_service", "notes": "Open community pathway content; consult release and attribution guidance." }, "data": { "input_types": [ "compound_list", "molecular_structures" ], "output_types": [ "pathway_data" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Pathway, species or identifier queries" ], "other_output_formats": [ "GPML, GMT, RDF and pathway images" ] }, "publications": [ { "citation": "Agrawal A, Balcı H, Hanspers K, et al. WikiPathways 2024: next generation pathway database. Nucleic Acids Res 52, D679-D689 (2024).", "type": "primary", "doi": "10.1093/nar/gkad960" } ], "common_uses": [ "Locate a pathway map for interpretation.", "Download pathway definitions for compatible enrichment tools." ], "scope": { "designed_for": [ "Locate a pathway map for interpretation." ], "not_designed_for": [ "Using pathway membership alone as proof of a mechanistic change." ], "requirements": [ "A species and pathway or molecular identifiers appropriate to the question." ], "considerations": [ "Inspect provenance, curation status and identifier coverage; pathway completeness differs between organisms and topics." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-08", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "reactome", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "workflow4metabolomics", "name": "Workflow4Metabolomics", "summary": "Create and share metabolomics processing and analysis workflows in a Galaxy environment.", "description": "Workflow4Metabolomics combines preprocessing, QC, normalization, statistics and annotation tools for MS-based metabolomics through a browser-based workflow system. This entry covers its MS workflows.", "links": { "primary": "https://workflow4metabolomics.org/", "documentation": "https://workflow4metabolomics.org/" }, "resource_types": [ "workflow_platform" ], "functions": { "primary": "workflow_reproducibility", "capabilities": [ "provenance_tracking" ] }, "platforms": [ "multi_platform" ], "interfaces": [ "web_app", "galaxy" ], "access": { "model": "free_web_service", "notes": "Free account on the public instance; individual tools and datasets retain their licenses." }, "data": { "input_types": [ "spectra", "feature_table", "sample_metadata" ], "output_types": [ "feature_table", "compound_list", "statistical_results", "report" ], "input_formats": [ "other" ], "output_formats": [ "other" ], "other_input_formats": [ "Files accepted by the chosen Galaxy tools" ], "other_output_formats": [ "Galaxy datasets, histories and workflows" ] }, "publications": [ { "citation": "Delporte C, Tremblay-Franco M, Guitton Y, et al. Workflow4Metabolomics (W4M): A User-Friendly Metabolomics Platform for Analysis of Mass Spectrometry and Nuclear Magnetic Resonance Data. Curr Protoc 5, e70095 (2025).", "type": "primary", "doi": "10.1002/cpz1.70095" } ], "common_uses": [ "Run a documented workflow without writing every analysis step.", "Share analysis histories and parameters with collaborators." ], "scope": { "designed_for": [ "Run a documented workflow without writing every analysis step." ], "not_designed_for": [ "Making an analysis valid merely because the workflow is reproducible." ], "requirements": [ "An account on a suitable Galaxy instance and data compatible with the chosen tools." ], "considerations": [ "W4M also supports NMR metabolomics, including workflows starting from FID data. This Navigator entry describes its MS-based applications.", "Record tool versions and histories; public-server quotas, installed tools and job availability vary by instance." ] }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed. Scope correction contributed by Yann Guitton (ORCID 0000-0002-4479-0636), W4M Coreteam, in https://github.com/scottjarmusch/metabolomics-navigator/issues/23." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-09-07", "updated_at": "2026-09-15", "last_editorial_review": "2026-09-07" }, "related_tools": [ { "slug": "xcms", "relationship": "complementary" }, { "slug": "camera", "relationship": "complementary" } ] }, { "$schema": "../../schemas/tool.schema.json", "slug": "xcms", "name": "xcms", "summary": "An open-source Bioconductor package for chromatographic mass spectrometry data preprocessing, feature detection, alignment, grouping, and visualization.", "links": { "primary": "https://bioconductor.org/packages/xcms/", "homepage": "https://bioconductor.org/packages/xcms/", "repository": "https://github.com/sneumann/xcms", "documentation": "https://bioconductor.org/packages/release/bioc/vignettes/xcms/inst/doc/xcms.html" }, "resource_types": [ "programming_package" ], "functions": { "primary": "preprocessing", "secondary": [ "quality_control", "normalization", "visualization_reporting" ], "capabilities": [ "chromatogram_building", "peak_detection", "retention_time_alignment", "gap_filling", "spectral_deconvolution" ] }, "platforms": [ "lc_ms", "gc_ms" ], "analysis_types": [ "untargeted", "metabolite_profiling" ], "interfaces": [ "r_package" ], "access": { "model": "open_source", "license": "Artistic-2.0", "notes": "The software license applies to the code; separately acquired libraries, vendor readers and hosted services may have their own terms." }, "technical": { "operating_systems": [ "windows", "macos", "linux", "cross_platform" ], "programming_languages": [ "R", "C++" ], "package_managers": [ "Bioconductor" ], "api_available": false }, "data": { "input_types": [ "raw_instrument_data", "spectra", "sample_metadata" ], "output_types": [ "feature_table", "statistical_results" ], "input_formats": [ "mzml", "mzxml", "netcdf" ], "output_formats": [ "csv_tsv" ] }, "publications": [ { "doi": "10.1021/ac051437y", "type": "primary", "citation": "Smith CA, Want EJ, O'Maille G, et al. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78, 779-787 (2006)." } ], "common_uses": [ "Build scriptable preprocessing workflows for untargeted chromatographic MS data.", "Detect chromatographic peaks and align retention times across samples.", "Export a feature-intensity table with sample metadata for downstream statistics after checking peak quality." ], "related_tools": [ { "slug": "openms", "relationship": "alternative" }, { "slug": "mzmine", "relationship": "alternative" } ], "maintenance": { "status": "active", "last_checked": "2026-08-21" }, "credits": [], "provenance": { "submitted_by": "curator", "developer_verified": false, "notes": "Documentation-based editorial pass on 6 September 2026: scope, interpretation limits and practical use reviewed against the linked official documentation and cited literature. Software execution, service availability and developer verification were not assessed." }, "status": { "entry": "published", "review": "editorially_reviewed", "created_at": "2026-08-04", "updated_at": "2026-09-08", "last_verified": "2026-08-21", "last_editorial_review": "2026-09-06" }, "scope": { "designed_for": [ "Reproducible, script-based preprocessing of LC-MS and GC-MS datasets in the R/Bioconductor ecosystem." ], "requirements": [ "R and Bioconductor plus the package dependencies appropriate to the selected xcms release." ], "considerations": [ "The package is code oriented rather than a standalone point-and-click workflow, so users benefit from basic R familiarity.", "Inspect representative chromatograms and alignment diagnostics; gap filling integrates signal in an expected region and does not guarantee a true detected metabolite." ], "not_designed_for": [ "Standalone confirmation of compound structures from chromatographic features." ] }, "acquisition": { "ms_levels": [ "ms1", "ms2" ], "strategies": [ "full_scan", "dda", "unspecified" ], "ion_mobility": "unspecified" }, "description": "xcms is a scriptable Bioconductor framework for processing chromatographically separated mass-spectrometry data in R. It provides algorithms for chromatographic peak detection, retention-time correction, correspondence/grouping across samples, filling missing feature intensities, and inspection of LC-MS and GC-MS datasets within reproducible analysis scripts.", "aliases": [ "XCMS" ] } ]