pyopenms

Analyze mass spectrometry data for proteomics and metabolomics workflows.

Updated May 17, 2026

Search Tags:#analysis #bioinformatics #metabolomics #mass spectrometry #proteomics

Ready to run

npx skills add https://github.com/galeep/plugin-place --skill pyopenms-galeep

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-proteomics-mass-spec/skills/pyopenms
Command: npx skills add https://github.com/galeep/plugin-place --skill pyopenms-galeep

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 provides Python bindings to the OpenMS library, enabling users to perform complex mass spectrometry analysis, including proteomics and metabolomics workflows.

Core Features & Use Cases

Proteomics Workflows: Feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines.
Metabolomics Analysis: Untargeted metabolomics preprocessing and analysis.
File I/O: Support for extensive file formats and algorithms.
Use Case: A researcher needs to analyze proteomics data from a mass spectrometer. This Skill allows them to read mass spectrometry files, process spectral data, detect features, identify peptides/proteins, and perform quantitative analysis.

Quick Start

Install PyOpenMS using pip:

uv pip install pyopenms

Load a mzML file:

import pyopenms as ms
exp = ms.MSExperiment()
ms.MzMLFile().load("data.mzML", exp)

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?▼

You can analyze mass spectrometry data in Python by using pyopenms, which provides Python bindings for the OpenMS library to read mzML files, process spectral data, and run proteomics and metabolomics workflows.

Can I perform untargeted metabolomics analysis and peptide identification using Python?▼

Yes, Python bindings for OpenMS support untargeted metabolomics preprocessing and proteomics workflows, enabling feature detection, peptide identification, and protein quantification for your mass spectrometry data.

How do I load and handle mzML mass spectrometry files in a Python script?▼

To load mzML files in Python, use the MSExperiment and MzMLFile classes from pyopenms, which provide extensive file I/O support for reading and manipulating mass spectrometry data formats.

Do I need the OpenMS library installed to run pyopenms for LC-MS/MS pipelines?▼

Yes, pyopenms requires the OpenMS library for computation and analysis, as it acts as Python bindings to handle complex LC-MS/MS pipelines, file I/O, and quantitative data processing tasks.

What is the best way to build a proteomics workflow for feature detection and quantification?▼

The best way to build a proteomics workflow is using pyopenms, which offers Python tools for feature detection, peptide identification, protein quantification, and comprehensive LC-MS/MS data processing.

Does pyopenms support quantitative analysis for untargeted metabolomics data?▼

Yes, pyopenms supports untargeted metabolomics preprocessing and quantitative analysis, enabling researchers to detect features and process spectral data from mass spectrometer outputs using Python scripts.