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alterlab-pyopenms

Process LC-MS/MS proteomics data with PyOpenMS for feature detection and identification.

58|9|Updated Mar 16, 2026
Search Tags:#data-analysis#mass-spectrometry#proteomics#feature-detection#python-api#identification#openms
One-click install
npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-pyopenms
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alterlab-pyopenms
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/bioinformatics/alterlab-pyopenms
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-pyopenms

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Facilitate end-to-end mass spectrometry data analysis for proteomics by providing a Python-based interface to OpenMS, enabling researchers to perform feature detection, identification, and quantification within a single framework.

Core Features & Use Cases

  • Comprehensive data handling: MSExperiment, MSSpectrum, MSChromatogram, and identification data management.
  • Feature detection, peptide/protein identification, and quantification workflows with export to standard OpenMS formats (featureXML, idXML, consensusXML).
  • Real-world scenario: process a raw LC-MS/MS run, detect features, map identifications, and generate exportable results for downstream analysis.

Quick Start

Load a mzML file, run peak picking and feature detection, and export the resulting feature map.

Frequently Asked Questions about alterlab-pyopenms

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

FAQPage Schema
How do I perform end-to-end mass spectrometry data analysis in Python?▼

You can perform end-to-end mass spectrometry data analysis in Python by using PyOpenMS to process raw LC-MS/MS runs, detect features, identify peptides, and export results to standard formats like featureXML and idXML.

What is the best way to handle MSExperiment objects for proteomics pipelines?▼

Handling MSExperiment objects for proteomics pipelines involves managing MSSpectrum and MSChromatogram data within PyOpenMS, enabling you to execute feature detection and peptide identification workflows with robust error handling.

How do I detect features and quantify proteins from raw LC-MS/MS data?▼

To detect features and quantify proteins from raw LC-MS/MS data, load your mzML file into PyOpenMS to run peak picking, execute feature detection, and export the resulting feature map to consensusXML.

Can I export proteomics identification results to idXML and featureXML formats?▼

Yes, you can export proteomics identification results to idXML and featureXML formats using the Python-based API workflows in PyOpenMS, which support mapping identifications and generating exportable outputs for downstream analysis.

Does PyOpenMS support Python-based workflows for spectrum and chromatogram data?▼

PyOpenMS supports Python-based workflows for spectrum and chromatogram data by providing comprehensive data handling capabilities for MSExperiment objects, ensuring robust validation throughout the proteomics analysis pipeline.

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