Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Workflow management and reproducibility

OpenMS

An open-source C++ library, command-line toolkit, and Python interface for reproducible mass spectrometry data processing in metabolomics and proteomics.

LC-MSTargeted metabolomicsUntargeted metabolomicsMetabolite profilingCommand line
Access toolSuggest an update

Overview

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.

What can this tool do?

Peak or feature detectionRetention-time alignmentGap fillingProvenance tracking

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.

Scientific fit

Primary function

Workflow management and reproducibility

Other functions

Data conversion and import, Preprocessing and feature extraction, Quantification and targeted analysis, Metabolite annotation and identification, Visualization and reporting

Analysis types

Targeted metabolomics, Untargeted metabolomics, Metabolite profiling

MS level

MS1, MS/MS

Acquisition

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

Ion mobility

Not specified

Data compatibility

Inputs

Raw instrument data, Mass spectra, Feature or peak table, Sample metadata

Outputs

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

Input formats

mzML, mzXML, MGF, CSV or TSV, mzData

Output formats

mzML, mzTab-M, CSV or TSV, featureXML, consensusXML

Scope and considerations

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.

Important 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.

Education

Learn this tool

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

CMFI Mass Spec Seminar #10 - Metabolomics Data Analysis with pyOpenMSFunctional Metabolomics Lab · Video Workshop · Needs review

Browse all education resources →

Access and documentation

Primary accessSource repositoryDocumentationTutorial

The software license applies to the code; separately acquired libraries, vendor readers and hosted services may have their own terms.

License: BSD-3-Clause

From tool to strategy

Strategies using OpenMS

Bioactivity-Based Molecular NetworkingNatural-product discovery · Journal of Natural ProductsFeature-Based Molecular Networking (FBMN)Molecular-family discovery · Nature MethodsTarget-decoy-controlled DIA metabolomicsMetabolite annotation · Nature Communications

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. 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). doi:10.1038/s41592-024-02197-7

Related tools

xcmsAlternative to · An open-source Bioconductor package for chromatographic mass spectrometry data preprocessing, feature detection, alignment, grouping, and visualization.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.patRoonComplementary to · Assemble reproducible non-target screening workflows for environmental mass spectrometry in R.SmartPeakComplementary to · Automate targeted and semi-targeted chromatographic data processing and quantification.

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. 2026-08-21 · Entry update

    Add files via upload

    View change · Entry at this revision
  4. 2026-08-20 · First committed entry

    Add files via upload

    View change · Entry at this revision

Full GitHub history