Analyze LC-MS data with Python bindings for OpenMS.
npx skills add https://github.com/OwnLabAI/ownlab --skill pyopenms-ownlabai
Please help me install this Agent Skill. Skill: pyopenms Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/pyopenms Command: npx skills add https://github.com/OwnLabAI/ownlab --skill pyopenms-ownlabai
PyOpenMS provides Python bindings to the OpenMS mass spectrometry library, enabling researchers to access advanced MS data processing, feature detection, peptide/protein identification, and quantification workflows directly from Python.
Install PyOpenMS, load your mzML data, and start exploring core objects with Python bindings.
High-intent search queries and answers about installing and using this skill.
Use PyOpenMS to load standard MS data formats like mzML or mzXML into Python objects such as MSExperiment, then bind to OpenMS workflows to run feature detection, peptide identification, and quantification on your proteomics or metabolomics datasets.
Yes, PyOpenMS provides Python bindings to load mzML and mzXML files into MSExperiment objects, allowing you to extract LC-MS features and populate FeatureMap data structures for downstream proteomics analysis.
You need a Python environment with PyOpenMS installed and access to standard MS data formats such as mzML, mzXML, or idXML to run LC-MS data analysis, feature detection, and peptide identification workflows.
Yes, PyOpenMS supports metabolomics and general LC-MS data workflows by providing Python-accessible APIs for feature detection, peptide identification, and quantification tasks within Python data analysis pipelines.
Use PyOpenMS to build an end-to-end Python pipeline that processes raw LC-MS datasets, extracts features via feature detection, identifies peptides, performs quantification, and generates analysis-ready results.
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