pyopenms

Name: pyopenms
Availability: InStock
Author: rubensliv

Expose OpenMS mass spectrometry analysis capabilities to Python.

15|2|Updated Dec 17, 2025

Search Tags:#metabolomics #data-analysis #mass-spectrometry #proteomics #pyopenms #openms

Ready to run

npx skills add https://github.com/rubensliv/k-dense-ai --skill pyopenms-rubensliv

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/pyopenms
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill pyopenms-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

PyOpenMS provides Python bindings to the OpenMS library, enabling Python users to access mass spectrometry data analysis workflows without C++.

Core Features & Use Cases

Access all major MS data formats (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML) and manipulate MS data structures (MSExperiment, MSSpectrum, FeatureMap, PeptideIdentification, etc.)
Build end-to-end MS data workflows in Python, including file I/O, feature detection, peptide/protein identification, and metabolomics analysis.
Use with proteomics/metabolomics pipelines to preprocess, analyze, and visualize results in Python.

Quick Start

Install the package, import pyopenms, load an mzML data file, and print basic statistics.

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 without using C++?▼

Mass spectrometry data analysis in Python is enabled through Python bindings to the OpenMS library, allowing you to process proteomics and metabolomics workflows without writing C++ code.

What mass spectrometry file formats can I read and write for proteomics workflows?▼

Proteomics workflows support reading and writing major MS data formats including mzML, mzXML, FASTA, mzIdentML, mzTab, pepXML, and protXML for file I/O operations.

How do I perform feature detection and peptide identification using Python?▼

Feature detection and peptide identification are performed by manipulating Pythonic data structures like MSExperiment, FeatureMap, and PeptideIdentification to build end-to-end MS data workflows.

Can I build end-to-end metabolomics data pipelines entirely in Python?▼

Metabolomics data pipelines can be built entirely in Python using OpenMS bindings, covering file I/O, feature detection, identification, and quantification for comprehensive analysis.

What are the limitations of using Python bindings for MS data analysis instead of the native library?▼

Python bindings for MS data analysis expose core OpenMS capabilities but may introduce overhead compared to native C++ execution, requiring Python environment management for large-scale proteomics or metabolomics datasets.