Does PyOpenMS integrate with other Python data libraries?

Absolutely. PyOpenMS data structures can be easily converted to Pandas DataFrames and NumPy arrays for visualization and machine learning.

What data formats does the PyOpenMS skill support?

It supports a wide range of formats including mzML, mzXML, TraML, mzTab, FASTA, pepXML, and mzIdentML for comprehensive data handling.

What signal processing capabilities are available?

The skill provides guidance on applying Gaussian filters, smoothing, and centroiding algorithms to raw spectral data.

Can I use this skill for metabolomics analysis?

Yes, it includes specialized workflows for untargeted metabolomics, including feature detection, retention time alignment, and compound annotation.

PyOpenMS Mass Spectrometry

Name: PyOpenMS Mass Spectrometry
Author: Microck

byMicrock

•

Data Science & ML

Simplifies mass spectrometry data analysis by providing Pythonic access to the OpenMS library for proteomics and metabolomics workflows.

About

PyOpenMS is a comprehensive Python interface for the OpenMS library, designed to facilitate complex computational mass spectrometry tasks directly within Claude Code. It enables researchers and developers to handle diverse data formats like mzML and FASTA, perform advanced signal processing, and execute end-to-end workflows for peptide identification and feature detection. Whether you are conducting untargeted metabolomics or large-scale proteomics experiments, this skill provides the domain-specific guidance and implementation patterns needed to process spectral data, align retention times, and integrate results with popular data science tools like Pandas and NumPy.

Key Features

Feature detection and linking across multiple samples for quantitative analysis
Multi-format file handling for mzML, mzXML, FASTA, and mzIdentML
Seamless integration with identification engines like Comet, Mascot, and MSGFPlus
81 GitHub stars
Advanced signal processing including smoothing, filtering, and centroiding
Native data conversion to Pandas DataFrames for downstream statistical analysis

Use Cases

Building automated LC-MS/MS proteomics pipelines for protein identification
Exporting processed mass spectrometry data into machine learning workflows
Conducting untargeted metabolomics preprocessing and feature alignment

About

Key Features

Feature detection and linking across multiple samples for quantitative analysis
Multi-format file handling for mzML, mzXML, FASTA, and mzIdentML
Seamless integration with identification engines like Comet, Mascot, and MSGFPlus
81 GitHub stars
Advanced signal processing including smoothing, filtering, and centroiding
Native data conversion to Pandas DataFrames for downstream statistical analysis

Use Cases

Building automated LC-MS/MS proteomics pipelines for protein identification
Exporting processed mass spectrometry data into machine learning workflows
Conducting untargeted metabolomics preprocessing and feature alignment