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pyopenms-mass-spectrometry

Process mass spectrometry data with PyOpenMS for proteomics and metabolomics.

298|27|Updated Feb 18, 2026
Search Tags:#metabolomics#mass spectrometry#data processing#proteomics#pyopenms#lc-ms/ms
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill pyopenms-mass-spectrometry
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyopenms-mass-spectrometry
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/proteomics-protein-engineering/pyopenms-mass-spectrometry
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill pyopenms-mass-spectrometry

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyopenms, numpy, pandas, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the complex process of analyzing mass spectrometry data, enabling efficient processing of proteomics and metabolomics workflows.

Core Features & Use Cases

  • Data Processing: Handles raw LC-MS/MS data (mzML, mzXML) for identification and quantification.
  • Feature Detection: Identifies and links chromatographic features across multiple samples.
  • Identification: Processes search engine results for peptide and protein identification with FDR control.
  • Use Case: Analyze raw LC-MS/MS data to identify differentially expressed proteins between two biological conditions, generating a list of significant proteins with associated FDR values.

Quick Start

Use the pyopenms-mass-spectrometry skill to load 'sample.mzML', smooth the spectra, and then pick peaks.

Frequently Asked Questions about pyopenms-mass-spectrometry

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

FAQPage Schema
How do I process raw mass spectrometry mzML files for peptide and protein identification?▼

PyOpenMS supports untargeted metabolomics pipelines by detecting and linking chromatographic features across multiple samples. It processes raw LC-MS/MS data to identify differentially expressed proteins between biological conditions with associated FDR values.

Can I use Python libraries like numpy and pandas with my mass spectrometry data processing workflow?▼

PyOpenMS handles mzML and mzXML file I/O for mass spectrometry data, performing signal processing like smoothing and peak picking. It then executes feature detection and linking to identify chromatographic features across multiple samples.

What is the best way to identify differentially expressed proteins between two biological conditions from LC-MS/MS data?▼

Signal processing for mass spectrometry data in this Skill includes spectral smoothing and peak picking algorithms provided by PyOpenMS. These steps refine raw LC-MS/MS data to prepare for accurate feature detection and peptide identification.

Does this approach support untargeted metabolomics pipelines and chromatographic feature linking across samples?▼

False Discovery Rate control in protein identification filters search engine results to ensure statistical reliability. This Skill processes search engine outputs to identify peptides and proteins while maintaining strict FDR thresholds for significant differential expression.

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