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pyopenms

Complete mass spectrometry analysis platform. Use for proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free and isobaric quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.

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@K-Dense-AI

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Development Tools

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PyOpenMS - Complete Mass Spectrometry Analysis Platform in Python

Skill Overview


PyOpenMS is a complete mass spectrometry analysis platform designed specifically for proteomics and metabolomics workflows. It provides end-to-end LC-MS/MS data processing capabilities, from feature detection and peptide/protein identification to quantification analysis and annotation.

Suitable Scenarios

  • Proteomics Research Analysis: Suitable for mass spectrometry–based protein identification, label-free quantification, and isotope labeling quantification studies. It supports a complete analysis workflow from raw spectra to a protein list.

  • Metabolomics Data Processing: Covers untargeted metabolite feature detection, adduct identification, accurate mass annotation, and GNPS/SIRIUS data export. It supports complex LC-MS metabolomics research.

  • Multi-Sample Quantification Studies: Provides multi-sample feature alignment, consensus linking, and generation of a quantification matrix. It supports statistical analysis needs for large-scale proteomics and metabolomics quantification studies.

    • Core Features

  • Ready-to-Use Analysis Scripts: Includes 20+ parameterized CLI tools covering common workflows, including data checking, format conversion, feature detection, quantification analysis, annotation, identification, and visualization. Prefer scripts to avoid rewriting code.

  • End-to-End LC-MS/MS Pipeline: Supports a complete analysis chain—from reading raw mzML/mzXML data, spectrum processing, feature detection, RT alignment, consensus linking to quantification matrix export—suitable for complex proteomics and metabolomics projects.

  • Chemical and Identification Analysis: Provides deep analysis functions such as protein digestion prediction, peptide mass calculation, theoretical spectrum generation, FDR estimation, adduct deconvolution, and accurate mass search against HMDB. It supports a complete cheminformatics workflow from sequence to identification.

    • Frequently Asked Questions

    What types of mass spectrometry data is PyOpenMS suitable for?


    PyOpenMS is designed for complex proteomics and metabolomics workflows. It is suitable for feature detection, peptide/protein identification, quantification analysis, and annotation scenarios. If you only need simple spectral comparisons and small-molecule library matching, it is recommended to use the matchms tool.

    How do I get started with PyOpenMS for analysis?


    It is recommended to start with the built-in ready-to-use scripts and use python scripts/<script name>.py --help to view all options. Common workflows include: using inspect_ms_data.py to check data, detect_features_metabo.py to detect metabolite features, and align_link_quantify.py for multi-sample quantification studies.

    What important changes are introduced in PyOpenMS version 3.5.0?


    Version 3.5.0 brings major API changes:
  • Removed FeatureFinder("centroided"); use FeatureFinderAlgorithmPicked instead.

  • idXML I/O requires using ms.PeptideIdentificationList().

  • DataFrame column names changed to lowercase rt/mz.

  • Adduct syntax changed to the Elements:Charge:Probability format.