What file formats does the PyOpenMS skill support?

It supports a wide range of industry-standard formats including mzML, mzXML, FASTA, pepXML, protXML, mzIdentML, TraML, and mzTab.

Can I use this skill for metabolomics research?

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

Does it integrate with standard Python data tools?

Absolutely. PyOpenMS is designed to work with the scientific Python stack, allowing you to export mass spectrometry data directly to Pandas DataFrames or NumPy arrays.

How does this skill assist with peptide identification?

The skill provides patterns for loading identification data, interfacing with search engines like Comet or Mascot, and applying False Discovery Rate (FDR) filtering to ensure results quality.

PyOpenMS Mass Spectrometry

Name: PyOpenMS Mass Spectrometry
Author: Activer007

byActiver007

0•

Data Science & ML

Analyzes complex mass spectrometry data for proteomics and metabolomics using Python bindings for the OpenMS library.

The PyOpenMS skill empowers Claude to perform sophisticated computational mass spectrometry analysis. It provides deep integration with the OpenMS library, enabling automated workflows for handling diverse file formats like mzML and FASTA, performing signal processing, detecting features, and identifying peptides or proteins. This skill is ideal for researchers and bioinformaticians who need to process LC-MS/MS data, apply False Discovery Rate (FDR) filtering, or conduct untargeted metabolomics studies while leveraging the power of the scientific Python ecosystem including Pandas, NumPy, and Scikit-learn.

Key Features

01Automated feature detection and linking across multiple experimental samples

02Comprehensive mass spectrometry file I/O support for mzML, FASTA, and mzIdentML

03Seamless data conversion to Pandas DataFrames for rapid statistical analysis

04Advanced signal processing including Gaussian smoothing, filtering, and centroiding

05Peptide and protein identification with integrated search engine support

060 GitHub stars

Use Cases

01Performing untargeted metabolomics preprocessing including retention time alignment

02Developing automated LC-MS/MS data processing and quality control pipelines

03Identifying and quantifying proteins in complex biological proteomics datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills pyopenms

For use in Claude.ai and ChatGPT

Download Skill