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pyopenms

Analyze mass spectrometry data using Python bindings for the OpenMS library.

557|98|Updated Nov 7, 2025
Search Tags:#data analysis#bioinformatics#metabolomics#mass spectrometry#proteomics#pyopenms
One-click install
npx skills add https://github.com/jimmc414/Kosmos --skill pyopenms-jimmc414
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/pyopenms
Command: npx skills add https://github.com/jimmc414/Kosmos --skill pyopenms-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies complex mass spectrometry data analysis, enabling researchers to process proteomics and metabolomics datasets efficiently.

Core Features & Use Cases

  • Data Handling: Load, process, and analyze various mass spectrometry file formats (mzML, mzXML, etc.).
  • Feature Detection: Identify and quantify molecular features in complex samples.
  • Identification: Integrate with search engines for peptide and protein identification.
  • Use Case: Analyze LC-MS/MS data to identify and quantify proteins in a biological sample, or process untargeted metabolomics data to discover biomarkers.

Quick Start

Use the pyopenms skill to load the mzML file 'sample.mzML' and print the number of spectra.

Frequently Asked Questions about pyopenms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I parse and process mzML mass spectrometry files in Python?▼

You can parse and process mzML mass spectrometry files in Python using pyopenms, which provides bindings to the OpenMS library for loading and analyzing standard proteomics and metabolomics data formats.

What Python libraries are available for untargeted metabolomics data analysis?▼

Python-based untargeted metabolomics data analysis is supported by pyopenms, which enables molecular feature detection and quantification in complex samples to help researchers discover potential biomarkers.

How can I perform peptide identification and quantification from LC-MS/MS data?▼

Peptide identification and quantification from LC-MS/MS data can be performed using pyopenms, facilitating integration with search engines and supporting FASTA and pepXML file formats for protein analysis.

Can I use pyopenms to handle both proteomics and metabolomics workflows?▼

Yes, pyopenms handles both proteomics and metabolomics workflows, offering signal processing and feature detection capabilities across multiple mass spectrometry data formats for comprehensive biological sample analysis.

What are the limitations of using Python for computational mass spectrometry analysis?▼

Computational mass spectrometry analysis via Python bindings relies on the underlying OpenMS library architecture, meaning processing speeds for large datasets are constrained by library overhead rather than native Python execution.

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