Automate mass spectrometry data analysis for proteomics workflows with PyOpenMS.
npx skills add https://github.com/Hung-3008/agusta --skill pyopenms-hung-3008
Please help me install this Agent Skill. Skill: pyopenms Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/pyopenms Command: npx skills add https://github.com/Hung-3008/agusta --skill pyopenms-hung-3008
PyOpenMS provides Python bindings to the OpenMS library to enable scalable, reproducible processing of mass spectrometry data, including reading diverse file formats, performing signal processing, and enabling identification and quantification workflows within Python environments.
Load an mzML file, run feature detection, and export a feature map for downstream analysis.
High-intent search queries and answers about installing and using this skill.
Automate mass spectrometry data analysis using Python bindings to the OpenMS library. This enables scalable, reproducible processing of LC-MS datasets, including file I/O, feature detection, peptide identification, and quantification within Python environments.
Read and export mzML, mzXML, idXML, mzIdentML, featureXML, and consensusXML files for LC-MS data processing. The Python bindings handle diverse format I/O and data structures like MSExperiment and FeatureMap to prepare results for downstream analysis.
Detect features and perform peak picking on mzML data by loading the file into an MSExperiment object. Signal processing prepares the mass spectrometry data, generating a FeatureMap for identification and subsequent export.
Peptide and protein identification integration alongside basic FDR post-processing workflows are supported. The Python bindings process mass spectrometry data structures to identify peptides across LC-MS datasets and export results for downstream analysis.
Handle metabolomics capabilities and ConsensusMap data structures using the OpenMS Python bindings. End-to-end workflows leverage MSExperiment, MSSpectrum, and FeatureMap objects to process mass spectrometry data for identification and quantification.
PyOpenMS, Python bindings, and OpenMS data structures like MSExperiment, MSSpectrum, and FeatureMap are required to run peptide and protein quantification. These components execute the scalable, reproducible mass spectrometry data analysis workflows.
lifangdaProcess LC-MS/MS data with peak picking and identification via pyOpenMS.
AlterLab-IEUProcess LC-MS/MS proteomics data with PyOpenMS for feature detection and identification.
jackspaceProvide a Python interface to OpenMS for mass spectrometry data analysis.
Yezez9Analyze proteomics and metabolomics mass spectrometry data with Python bindings.
