pyopenms

Analyze mass spectrometry data for proteomics and metabolomics workflows.

2|Updated Jun 4, 2026

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

Ready to run

npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill pyopenms-lord1egypt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/pyopenms
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill pyopenms-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the complexity of processing large-scale mass spectrometry data, providing a unified platform for proteomics and metabolomics workflows that would otherwise require manual, error-prone pipeline construction.

Core Features & Use Cases

Comprehensive MS Processing: Handles file I/O, signal processing, feature detection, and identification for proteomics and metabolomics.
Advanced Workflows: Enables complex tasks like peptide identification, protein quantification, and adduct detection across multiple samples.
Use Case: Researchers can use this skill to automate the detection and linking of features across multiple LC-MS runs, significantly accelerating the discovery of differential metabolites or proteins in clinical samples.

Quick Start

Use the pyopenms skill to load a mass spectrometry file and perform feature detection on the raw spectral data.

Frequently Asked Questions about pyopenms

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

FAQPage Schema

How do I perform feature detection and peptide identification on raw mass spectrometry data?▼

You can perform feature detection and peptide identification on raw mass spectrometry data by using a unified platform for proteomics and metabolomics workflows that handles file I/O and signal processing. This automates complex pipeline construction.

What is the best way to automate linking features across multiple LC-MS runs for proteomics?▼

The best way to link features across multiple LC-MS runs is using an advanced computational platform that enables feature detection and linking across samples. This accelerates the discovery of differential proteins in clinical samples.

Can I use Python for quantitative analysis and statistical FDR control in metabolomics workflows?▼

Yes, you can use Python for quantitative analysis and statistical FDR control in metabolomics workflows. The platform integrates with standard bioinformatics databases and supports high-performance signal processing for accurate results.

Does this approach support diverse MS file formats for both proteomics and metabolomics processing?▼

Yes, this approach supports diverse MS file formats for both proteomics and metabolomics processing. It provides comprehensive computational capabilities for file I/O, signal processing, and adduct detection across multiple samples.

How do I detect differential metabolites across multiple LC-MS samples without manual pipeline construction?▼

To detect differential metabolites across multiple LC-MS samples without manual pipelines, use a computational platform that automates feature detection, linking, and quantitative analysis. This prevents error-prone manual setup.

Why use pandas and numpy for mass spectrometry data analysis instead of standard bioinformatics tools?▼

Using pandas and numpy for mass spectrometry data analysis enables customized statistical FDR control and quantitative analysis within Python. This integration allows high-performance signal processing alongside standard bioinformatics database workflows.