What mass spectrometry file formats are supported?

The PyOpenMS skill supports a comprehensive range of formats including mzML, mzXML, TraML, mzTab, FASTA, pepXML, protXML, and mzIdentML.

Can I use this skill for metabolomics research?

Yes, it provides specific workflows for untargeted metabolomics, covering data preprocessing, feature detection, and retention time alignment.

Does it integrate with standard Python data tools?

Yes, it is designed to work seamlessly with NumPy, Pandas for data manipulation, and Matplotlib/Seaborn for visualization.

How does the skill handle peptide identification?

It allows you to load identification data from major search engines like Comet and Mascot, and perform post-processing tasks like False Discovery Rate (FDR) filtering.

PyOpenMS Mass Spectrometry Analysis

Name: PyOpenMS Mass Spectrometry Analysis
Author: K-Dense-AI

byK-Dense-AI

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3,719

Data Science & ML

Analyzes mass spectrometry data using Python bindings for OpenMS to process complex proteomics and metabolomics workflows.

About

This skill empowers Claude with specialized capabilities for computational mass spectrometry by leveraging the PyOpenMS library. It facilitates the end-to-end processing of proteomics and metabolomics data, from handling diverse file formats like mzML and FASTA to performing sophisticated signal processing, feature detection, and peptide identification. It is an essential tool for bioinformaticians and researchers needing to automate spectral analysis, manage identification results, and build reproducible scientific pipelines within their development environment.

Key Features

Peptide and protein identification with search engine integration and FDR filtering
3,719 GitHub stars
Direct export of mass spectrometry data structures to Pandas DataFrames
Automated feature detection and linking across multiple samples
Advanced signal processing including Gaussian smoothing and centroiding
Comprehensive file I/O for mzML, mzXML, FASTA, and identification formats

Use Cases

Preprocessing and aligning raw data for untargeted metabolomics studies
Standardizing mass spectrometry data for downstream machine learning analysis
Building automated proteomics identification and quantification pipelines

About

Key Features

Peptide and protein identification with search engine integration and FDR filtering
3,719 GitHub stars
Direct export of mass spectrometry data structures to Pandas DataFrames
Automated feature detection and linking across multiple samples
Advanced signal processing including Gaussian smoothing and centroiding
Comprehensive file I/O for mzML, mzXML, FASTA, and identification formats

Use Cases

Preprocessing and aligning raw data for untargeted metabolomics studies
Standardizing mass spectrometry data for downstream machine learning analysis
Building automated proteomics identification and quantification pipelines