pyopenms

Analyze proteomics and metabolomics mass spectrometry data with Python bindings.

Updated Mar 10, 2026

Search Tags:#data analysis #bioinformatics #metabolomics #mass spectrometry #proteomics #lc-ms/ms

Ready to run

npx skills add https://github.com/Yezez9/Research-Agent --skill pyopenms-yezez9

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/pyopenms
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill pyopenms-yezez9

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive platform for mass spectrometry data analysis, enabling researchers to process complex proteomics and metabolomics datasets efficiently.

Core Features & Use Cases

Data Import/Export: Supports numerous mass spectrometry file formats (mzML, mzXML, etc.).
Signal Processing: Includes tools for smoothing, filtering, and centroiding spectral data.
Feature Detection & Linking: Identifies and groups chromatographic features across samples for quantitative analysis.
Identification Workflows: Facilitates peptide and protein identification and FDR control.
Use Case: Analyze raw LC-MS/MS data to identify and quantify thousands of 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 analyze raw LC-MS/MS data for proteomics in Python?▼

To analyze raw LC-MS/MS data for proteomics in Python, you can use this Skill to load mzML files and run peptide identification workflows. It provides comprehensive algorithms for identifying and quantifying thousands of proteins in biological samples.

What is the best way to process untargeted metabolomics data to discover biomarkers?▼

Processing untargeted metabolomics data to discover biomarkers requires identifying and grouping chromatographic features across samples. This Skill supports complete metabolomics workflows including signal processing, feature detection, and linking for quantitative analysis.

Can I read and export mzML mass spectrometry files using Python?▼

Yes, you can read and export mzML mass spectrometry files using Python. This Skill supports numerous mass spectrometry file formats including mzML and mzXML, enabling efficient data import and export for downstream computational analysis.

How do I perform signal smoothing and centroiding on spectral data?▼

To perform signal smoothing and centroiding on spectral data, this Skill provides dedicated signal processing tools. These functions allow you to filter and refine raw mass spectrometry data before moving on to feature detection.

Does this platform support false discovery rate control for protein identification?▼

Yes, this platform supports false discovery rate (FDR) control for protein identification. It facilitates complete identification workflows, allowing you to confidently validate peptide and protein identification results from your mass spectrometry data.

When do I need feature detection and linking in mass spectrometry analysis?▼

You need feature detection and linking in mass spectrometry analysis when performing quantitative comparisons across multiple samples. This process identifies chromatographic features and groups them, which is essential for accurately quantifying proteins or metabolites.