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lib-pyopenms

Provides Python interface to OpenMS library for mass spectrometry analysis of proteomics and metabolomics data.

Updated Mar 13, 2026
Search Tags:#data analysis#bioinformatics#metabolomics#mass spectrometry#proteomics#pyopenms
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pyopenms
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lib-pyopenms
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-pyopenms
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pyopenms

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive platform for analyzing mass spectrometry data, streamlining complex proteomics and metabolomics workflows from raw data processing to identification and quantification.

Core Features & Use Cases

  • Data Handling: Read and write numerous mass spectrometry file formats (mzML, mzXML, etc.).
  • Signal Processing: Perform smoothing, filtering, peak picking, and normalization on spectral data.
  • Feature Detection & Linking: Identify and group chromatographic features across samples for quantitative analysis.
  • Identification: Process peptide and protein identification results, including FDR control.
  • Metabolomics: Specialized tools for untargeted metabolomics analysis.
  • Use Case: Analyze a large mzML file to detect features, identify peptides, and quantify proteins across multiple samples, generating a comprehensive report.

Quick Start

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

Frequently Asked Questions about lib-pyopenms

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

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

This Python interface reads mzML and other mass spectrometry file formats directly, providing access to raw spectral data for downstream signal processing, feature detection, and quantitative analysis workflows.

What is the best way to detect chromatographic features across multiple LC-MS/MS samples?▼

The best way to detect chromatographic features across multiple LC-MS/MS samples is using built-in feature detection and linking algorithms, which group features across samples for quantitative proteomics and metabolomics analysis.

Can I perform peptide and protein identification with FDR control using Python?▼

Yes, you can perform peptide and protein identification with false discovery rate (FDR) control in Python, processing identification results directly within the mass spectrometry analysis pipeline.

Does this platform support untargeted metabolomics signal processing and peak picking?▼

Yes, the platform supports untargeted metabolomics by providing signal processing tools for smoothing, filtering, normalization, and peak picking on raw spectral data.

How do I quantify proteins across multiple mass spectrometry samples?▼

You can quantify proteins across multiple mass spectrometry samples by linking detected chromatographic features and processing peptide identification results to generate comprehensive quantitative reports.

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