pyopenms

Analyze mass spectrometry data for proteomics and metabolomics via Python bindings.

3|Updated Apr 17, 2026

Search Tags:#data analysis #metabolomics #mass spectrometry #proteomics #OpenMS

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pyopenms-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/pyopenms
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pyopenms-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openms, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies complex mass spectrometry data analysis tasks by providing Python bindings to the OpenMS library, enabling users to efficiently process proteomics and metabolomics data.

Core Features & Use Cases

Proteomics and Metabolomics Analysis: Handle mass spectrometry file formats, process spectral data, detect features, identify peptides/proteins, and perform quantitative analysis.
File I/O and Data Formats: Support extensive file formats like mzML, mzXML, TraML, mzTab, and more.
Signal Processing: Apply smoothing, filtering, centroiding, and normalization to raw spectral data.
Feature Detection: Detect and link features across spectra and samples for quantitative analysis.
Peptide and Protein Identification: Integrate with search engines and process identification results.
Metabolomics Analysis: Perform untargeted metabolomics preprocessing and analysis.
Use Case: A researcher might use this Skill to analyze proteomics data from a mass spectrometry experiment, detect and identify peptides and proteins, and perform quantification.

Quick Start

Use the pyopenms skill to load and analyze a mass spectrometry file 'sample.mzML'.

Frequently Asked Questions about pyopenms

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

FAQPage Schema

How do I analyze mass spectrometry data in Python for proteomics?▼

To analyze mass spectrometry data in Python for proteomics, you can use pyopenms bindings to process spectral data, detect features, and identify peptides. It requires the OpenMS library to be installed in your environment to function.

Can I use Python to detect and quantify peptides from mzML files?▼

Yes, you can use Python to detect and quantify peptides from mzML files. These Python bindings support extensive mass spectrometry file formats including mzML, enabling feature detection and quantitative protein analysis directly.

Does pyopenms support untargeted metabolomics preprocessing and analysis?▼

Yes, pyopenms supports untargeted metabolomics preprocessing and analysis. It provides Python bindings to the OpenMS library, allowing researchers to process spectral data and perform metabolomics analysis alongside proteomics tasks.

What signal processing operations can I apply to raw spectral data using Python?▼

You can apply signal processing operations like smoothing, filtering, centroiding, and normalization to raw spectral data using Python. These bindings expose OpenMS signal processing functions to prepare mass spectrometry data for feature detection.

Do I need to install OpenMS separately to use these Python bindings?▼

Yes, you need to install the OpenMS library separately to use these Python bindings. The Skill provides Python access to OpenMS functionality for mass spectrometry data analysis but depends on the underlying OpenMS installation being present.

How do I link features across mass spectrometry samples for quantitative analysis?▼

To link features across mass spectrometry samples for quantitative analysis, use the feature detection capabilities provided by these Python bindings. They enable detecting and linking features across spectra and samples to support quantification of peptides and proteins.