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Pyopenms

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davila7·vlatest·backend·MIT

PyopenMS enables Python users to interact with MS datasets and perform spectral analysis, streamlining workflows for mass spectrometry data processing.

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Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill pyopenms -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

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Live repository

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About this skill

What it does

This skill provides access to the pyopenms library, a powerful open-source software suite for processing and analyzing mass spectrometry data. It enables tasks such as peptide identification, protein quantification, and proteomics workflow automation directly within an AI agent environment. The skill allows users to leverage advanced algorithms for complex biological data analysis.

When to use it

  • Proteomics Data Analysis: Analyze raw mass spectrometry data files (e.g., .mzML) to identify peptides and proteins.
  • Bioinformatics Research: Perform protein quantification experiments and generate reports for research publications.
  • Drug Discovery: Support drug discovery efforts by analyzing peptide and protein profiles in biological samples.
  • Metabolomics Studies: Integrate with metabolomics workflows for comprehensive biological data analysis.

Key capabilities

  • Mass spectrometry data import and processing
  • Peptide identification using various search engines
  • Protein quantification based on spectral counts or label-free methods
  • Proteomics workflow automation

Example prompts

  • "Analyze this .mzML file and identify the peptides present."
  • "Quantify the abundance of protein X in these two samples."
  • "Run a peptide identification search against the UniProt database for this mass spectrometry data."

Tips & gotchas

  • Requires familiarity with proteomics workflows and mass spectrometry data formats.
  • The pyopenms library can be computationally intensive; ensure sufficient resources are available.