Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Preprocessing and feature extraction

MZmine

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.

LC-MSDirect-infusion MSMALDI-MSImaging mass spectrometryGC-MS
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Documented workflows

Published software workflows and their original methodological context. Check current software documentation for version-specific changes.

GC-EI-MS processing and annotation with MZmine

The GC-MS procedure in the MZmine Nature Protocols workflow converts GC-EI-MS data into deconvoluted component spectra and aligned quantitative features. These outputs can be annotated against EI spectral libraries and exported for downstream analysis or GNPS-compatible GC-MS workflows.

  1. Import GC-EI-MS data

    Load supported GC-MS data and configure mass detection and chromatogram construction for electron-ionization spectra.

  2. Detect chromatographic signals

    Build ion chromatograms and detect chromatographic peaks using settings appropriate to peak width, signal-to-noise, and the GC method.

  3. Deconvolute EI mass spectra

    Resolve overlapping chromatographic signals into representative component spectra using a spectral-deconvolution workflow.

  4. Align components across samples

    Align deconvoluted features/spectra across the sample set and apply appropriate quality filters.

  5. Annotate and export

    Match deconvoluted EI spectra against suitable spectral libraries and export quantitative tables and representative spectra for downstream analysis.

Requirements

  • GC-EI-MS data of sufficient chromatographic resolution and signal quality for deconvolution.

Considerations

  • Deconvolution quality strongly affects downstream spectral-library matching; chromatographic and spectral parameters should be tuned to the instrument and sample complexity.
  • The published procedure targets MZmine 3; current MZmine 4 documentation should be used to map the procedure onto current modules.

Heuckeroth S, Damiani T, Smirnov A, et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nature Protocols 19, 2597–2641 (2024).

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MS-imaging processing and annotation with MZmine

The imaging procedure in the MZmine Nature Protocols workflow processes spatially resolved MS or IMS-MS data into features that can be visualized as ion images and linked to spectral/annotation evidence. The workflow is applicable to imaging modalities supported by MZmine and can integrate ion mobility where available.

  1. Import spatial MS data

    Import supported imaging data while preserving spatial coordinates and, where available, ion-mobility information.

  2. Detect and organize imaging features

    Apply mass detection and feature-processing steps appropriate to the imaging acquisition to generate spatially resolved ion features.

  3. Visualize spatial distributions

    Inspect ion images and, where available, mobility or spectral dimensions to assess localization and data quality.

  4. Annotate spatial features

    Use available MS/MS, spectral libraries, lipid annotation, or other MZmine annotation modules to add chemical context to spatially resolved features.

  5. Export reproducible results

    Save processing configurations, feature information, images, and compatible outputs for downstream statistical or annotation workflows.

Requirements

  • Imaging-MS data in a format supported by MZmine; ion-mobility information is optional and instrument dependent.

Considerations

  • Spatial resolution, normalization, matrix/background effects, and annotation confidence require imaging-specific quality assessment beyond generic LC-MS preprocessing.
  • The published procedure targets MZmine 3; consult current MZmine 4 imaging documentation for current module names and supported acquisition types.

Heuckeroth S, Damiani T, Smirnov A, et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nature Protocols 19, 2597–2641 (2024).

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Untargeted LC-(IMS)-MS processing and annotation with MZmine

This strategy captures the LC-(IMS)-MS procedure published for MZmine 3: raw spectral data are converted into aligned quantitative features linked to isotope patterns, ion mobility where present, and MS/MS spectra, then annotated and exported for downstream tools. Current MZmine 4 uses the same broad processing logic while providing updated modules and wizard-driven workflows.

  1. Import and inspect LC-MS or LC-IMS-MS data

    Import supported vendor/open data, apply appropriate mass detection, and inspect chromatographic and spectral quality before feature processing.

  2. Build chromatograms and resolve features

    Construct extracted-ion chromatograms and resolve chromatographic peaks using experiment-appropriate intensity, mass-tolerance, retention-time, and peak-shape settings.

  3. Group isotopes/ions and align samples

    Group isotopic and related ion signals, align features across samples, perform gap filling where justified, and apply blank/QC or other quality filters.

  4. Annotate features

    Use spectral-library matching, local databases, lipid annotation, and ion-identity relationships as appropriate to generate feature annotations.

  5. Export to downstream annotation/networking tools

    Export aligned feature tables and spectra for GNPS FBMN/IIMN, SIRIUS, MetaboAnalyst, or other downstream analyses while retaining the MZmine batch configuration for reproducibility.

Requirements

  • Appropriate raw/open MS data and experiment-specific processing parameters; DDA MS/MS is especially useful for feature annotation.

Considerations

  • The published procedure targets MZmine 3 and should be adapted using current MZmine 4 documentation and wizard defaults rather than copied blindly.
  • Batch files and software version should be retained and reported to support reproducibility.

Heuckeroth S, Damiani T, Smirnov A, et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nature Protocols 19, 2597–2641 (2024).

Read the source method

Overview

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.

What can this tool do?

Adduct and isotope groupingBatch-effect correctionBlank and contaminant filteringChromatogram buildingGap fillingInteractive dashboardIon identity networkingSpectral-library matchingMS/MS processingMultivariate statisticsNetwork visualizationPeak or feature detectionRetention-time alignmentSpectral deconvolutionStructure-database searchingUnivariate statistics

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.

Scientific fit

Primary function

Preprocessing and feature extraction

Other functions

Metabolite annotation and identification, Molecular networking and chemical similarity, Normalization and batch correction, Quality control and filtering, Spectral processing and comparison, Statistical analysis, Visualization and reporting

Analysis types

Lipidomics, Metabolite profiling, Spatial metabolomics, Targeted metabolomics, Untargeted metabolomics, Volatilomics

MS level

MS1, MS/MS

Acquisition

Full-scan MS, Data-dependent acquisition (DDA), Data-independent acquisition (DIA)

Ion mobility

Ion mobility supported

Data compatibility

Inputs

Raw instrument data, Mass spectra

Outputs

Feature or peak table, Mass spectra, Compound list or annotations, Human-readable report

Input formats

Vendor raw format, mzML, mzXML, netCDF or CDF, imzML

Output formats

CSV or TSV, MGF, MSP, mzTab-M

Scope and considerations

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.

Important 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.

Education

Learn this tool

External tutorials and workshops collected by Navigator. Entries marked Needs review have not been checked end to end.

GNPS FBMN - American Gut / MZmine2 tutorialDorrestein Lab · Video · Needs reviewGNPS feature finding with MZmine 2 workshopDorrestein Lab · Video Workshop · Needs reviewGNPS Ion-Identity Networking, Native MetabolomicsDorrestein Lab · Video Workshop · Needs reviewMZmine Learners cornermzmine project · Training hubMZmine workshopsmzmine project · Training hubNTMSS2023 Day2 BONUS - MZmine 3 New FeaturesFunctional Metabolomics Lab · Video Workshop · Needs reviewNTMSS2023 Day2 S2 - Data preprocessing in MZmine 3 IFunctional Metabolomics Lab · Video Workshop · Needs reviewNTMSS2026 Day2 S1 - Robin Schmid - mzmine part3Functional Metabolomics Lab · Video Workshop · Needs reviewNTMSS2026Day1 S6 - Robin Schmid - mzmine part1Functional Metabolomics Lab · Video Workshop · Needs reviewNTMSS2026Day1 S7 - Robin Schmid - mzmine part2Functional Metabolomics Lab · Video Workshop · Needs reviewVMOL mzmine series 1 — Non-targeted LC-MS/MS (Robin Schmid)Functional Metabolomics Lab / VMOL · Video · Needs reviewVMOL mzmine series 2 — Multimodal data processingFunctional Metabolomics Lab / VMOL · Video · Needs reviewVMOL mzmine series 3 — Ion-mobility data processingFunctional Metabolomics Lab / VMOL · Video · Needs reviewVMOL mzmine series 4 — MSn library generationFunctional Metabolomics Lab / VMOL · Video · Needs review

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Access and documentation

Primary accessSource repositoryDocumentationIssue tracker

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

From tool to strategy

Strategies using MZmine

Bioactivity-Based Molecular NetworkingNatural-product discovery · Journal of Natural ProductsFeature-Based Molecular Networking (FBMN)Molecular-family discovery · Nature MethodsIon Identity Molecular Networking (IIMN)Metabolite annotation · Nature CommunicationsIterative exclusion-list MS/MS acquisitionMetabolite annotation · Journal of the American Society for Mass SpectrometryNative MS metabolomics for metal-binding compoundsMetal-binding compound discovery · Nature Chemistry

Evidence and review

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.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. Schmid R, Heuckeroth S, Korf A, et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol 41, 447-449 (2023). doi:10.1038/s41587-023-01690-2
  2. 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). doi:10.1038/s41596-024-00996-y
  3. 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). doi:10.1021/acs.analchem.8b05493

    Introduces a lipid-annotation extension implemented within MZmine 2.

Related tools

GNPSIntegrates with · A web-based mass-spectrometry ecosystem for molecular networking, spectral-library search, repository-scale search, interactive visualization, knowledgebase curation, and public-data integration.SIRIUSIntegrates with · 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.MS-DIALAlternative to · A graphical and command-line workbench for untargeted metabolomics and lipidomics data processing across multiple chromatography and mass spectrometry platforms.xcmsAlternative to · An open-source Bioconductor package for chromatographic mass spectrometry data preprocessing, feature detection, alignment, grouping, and visualization.`iliComplementary to · Web-based software for interactive visualization of molecular distributions on 2D images and 3D surfaces.asariComplementary to · Process high-resolution mass-spectrometry data into aligned feature tables.msconvertComplementary to · Convert mass-spectrometry files into open formats for downstream analysis.GNPS2 Feature-Based Molecular Networking workflowIntegrates with · Dedicated GNPS2 workflow that combines an aligned LC-MS feature table with MS/MS spectra to build quantitative molecular networks.
More related tools (2)
MS2DECIDEIntegrates with · Combine GNPS, ISDB-LOTUS and SIRIUS annotations into scores for natural-product feature prioritization.MSnLibIntegrates with · Open multistage fragmentation reference spectra and a reproducible library-generation workflow implemented with mzmine.

Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-09-08 · Entry update

    Audit core publications across existing tools

    View change · Entry at this revision
  2. 2026-09-07 · Entry update

    Expand and document 31 metabolomics tools and resources

    View change · Entry at this revision
  3. 2026-08-24 · Entry update

    Complete GNPS SIRIUS and MZmine tool ecosystems

    View change · Entry at this revision
  4. 2026-08-24 · Entry update

    Add GNPS, SIRIUS, and MZmine content batch

    View change · Entry at this revision
  5. 2026-08-21 · Entry update

    Add files via upload

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  6. 2026-08-20 · First committed entry

    Add files via upload

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Full GitHub history