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PY
pyOpenMS
Maintainer K-Dense Inc. · Last updated April 1, 2026
PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis.
Original source
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pyopenms
- Maintainer
- K-Dense Inc.
- License
- 3 clause BSD license
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- PyOpenMS provides Python bindings to the OpenMS library for computational mass spectrometry, enabling analysis of proteomics and metabolomics data. Use for handling mass spectrometry file formats, processing spectral data, detecting features, identifying peptides/proteins, and performing quantitative analysis.
- exp = ms.MSExperiment() ms.MzMLFile().load("data.mzML", exp).
Source Doc
Excerpt From SKILL.md
Installation
Install using uv:
Verify installation:
Core Capabilities
PyOpenMS organizes functionality into these domains:
1. File I/O and Data Formats
Handle mass spectrometry file formats and convert between representations.
Supported formats: mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, mzIdentML, featureXML, consensusXML, idXML
Basic file reading:
import pyopenms as ms
Use cases
- Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines.
- Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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