What file formats does the pyOpenMS skill support?

It supports standard mass spectrometry formats including mzML, mzXML, FASTA, mzTab, mzIdentML, and TraML for comprehensive data handling.

Can I use this for both proteomics and metabolomics?

Yes, it includes specialized tools for protein digestion and peptide identification as well as metabolite adduct detection and isotope pattern matching.

Does it support raw data processing and centroiding?

Absolutely. The skill can handle profile-to-centroid conversion (peak picking), signal smoothing, and multi-sample feature detection directly from raw LC-MS data.

Can I visualize MS data with this skill?

Yes, the skill provides implementation patterns for generating TIC/BPC plots and individual spectra visualizations using pyOpenMS and matplotlib.

pyOpenMS Proteomics Toolkit

Name: pyOpenMS Proteomics Toolkit
Author: lifangda

bylifangda

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Data Science & ML

Processes mass spectrometry data for proteomics and metabolomics analysis using the pyOpenMS library.

About

The pyOpenMS skill provides a comprehensive Python interface for high-performance mass spectrometry data analysis within Claude. It enables sophisticated LC-MS/MS workflows, including raw data parsing (mzML/mzXML), signal smoothing, peak picking, and feature detection. Designed for bioinformaticians and researchers, this skill facilitates chemical mass calculations, protein digestion, and peptide identification while integrating seamlessly with established tools like Comet and Mascot. It is particularly useful for automating complex proteomics or metabolomics pipelines and performing detailed isotopic distribution analysis.

Key Features

Peptide identification workflows with FDR control and protein digestion
Metabolomics capabilities including adduct detection and isotope matching
Native support for mzML, mzXML, and FASTA file processing
Advanced signal processing including Gaussian smoothing and peak picking
Sophisticated chemical mass calculations and isotopic distribution generation
16 GitHub stars

Use Cases

Calculating theoretical peptide masses and fragmentation patterns for identification
Automating LC-MS/MS data processing pipelines from raw files to quantified features
Performing quality control and visualization of mass spectrometry runs via TIC and BPC plots

About

Key Features

Peptide identification workflows with FDR control and protein digestion
Metabolomics capabilities including adduct detection and isotope matching
Native support for mzML, mzXML, and FASTA file processing
Advanced signal processing including Gaussian smoothing and peak picking
Sophisticated chemical mass calculations and isotopic distribution generation
16 GitHub stars

Use Cases

Calculating theoretical peptide masses and fragmentation patterns for identification
Automating LC-MS/MS data processing pipelines from raw files to quantified features
Performing quality control and visualization of mass spectrometry runs via TIC and BPC plots