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Skillspyopenms
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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.

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

  1. 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.

  2. 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.

  3. 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

  1. 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.

  2. 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.

  3. 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.