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pyopenms

Analyze mass spectrometry data for proteomics and metabolomics workflows.

13|3|Updated Jun 10, 2026
Search Tags:#data analysis#bioinformatics#metabolomics#mass spectrometry#proteomics
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill pyopenms-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/pyopenms
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill pyopenms-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyopenms, pandas, numpy, scikit-learn, matplotlib, seaborn, rpy2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill empowers you to process complex mass spectrometry data for proteomics and metabolomics research with ease.

Core Features & Use Cases

  • File I/O and Data Formats: Handle and convert a wide range of mass spectrometry file formats.
  • Signal Processing: Apply signal processing techniques for raw spectral data manipulation.
  • Feature Detection: Detect and link features for quantitative analysis.
  • Peptide and Protein Identification: Integrate with various search engines for accurate identification.
  • Metabolomics Analysis: Perform preprocessing and analysis for untargeted metabolomics.
  • Use Case: Process high-throughput MS/MS data from LC-MS experiments for metabolite identification and quantification.

Quick Start

To load and analyze data using PyOpenMS, execute the following Python code: ```python import pyopenms as ms exp = ms.MSExperiment() ms.MzMLFile().load("sample.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 for proteomics and metabolomics in Python?▼

You can analyze mass spectrometry data using Python by loading raw files like MzML into an experiment object, then applying signal processing, feature detection, and peptide identification techniques to process the spectra.

What is the best way to handle and convert mass spectrometry file formats for LC-MS experiments?▼

Handling mass spectrometry file formats involves using specialized I/O modules to load, convert, and manage a wide range of raw spectral data files, enabling seamless preprocessing for high-throughput LC-MS experiments.

How do I detect and link features for quantitative untargeted metabolomics analysis?▼

Detecting and linking features for untargeted metabolomics requires applying signal processing to raw spectral data to identify quantitative traits, which are then linked for further statistical analysis using libraries like scikit-learn.

Can I use pandas and scikit-learn for downstream analysis of mass spectrometry feature data?▼

Yes, you can use pandas and scikit-learn for downstream analysis of mass spectrometry data, leveraging numpy arrays and dataframes to perform statistical analysis and machine learning on detected features.

Does this approach support integrating search engines for peptide and protein identification?▼

Yes, peptide and protein identification supports integration with various search engines, allowing you to accurately match processed mass spectrometry data against known sequences for high-throughput proteomics research.

What Python dependencies are required to process MS/MS data for metabolite identification?▼

Processing MS/MS data for metabolite identification requires Python dependencies including pyopenms for core functions, pandas and numpy for data manipulation, and matplotlib and seaborn for visualization.

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