Processes mass spectrometry data for proteomics and metabolomics analysis using the pyOpenMS library.
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
01Peptide identification workflows with FDR control and protein digestion
02Metabolomics capabilities including adduct detection and isotope matching
03Native support for mzML, mzXML, and FASTA file processing
04Advanced signal processing including Gaussian smoothing and peak picking
05Sophisticated chemical mass calculations and isotopic distribution generation
0616 GitHub stars
Use Cases
01Calculating theoretical peptide masses and fragmentation patterns for identification
02Automating LC-MS/MS data processing pipelines from raw files to quantified features
03Performing quality control and visualization of mass spectrometry runs via TIC and BPC plots
