Expose OpenMS mass spectrometry analysis capabilities to Python.
npx skills add https://github.com/rubensliv/k-dense-ai --skill pyopenms-rubensliv
Please help me install this Agent Skill. Skill: pyopenms Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/pyopenms Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill pyopenms-rubensliv
PyOpenMS provides Python bindings to the OpenMS library, enabling Python users to access mass spectrometry data analysis workflows without C++.
Install the package, import pyopenms, load an mzML data file, and print basic statistics.
High-intent search queries and answers about installing and using this skill.
Mass spectrometry data analysis in Python is enabled through Python bindings to the OpenMS library, allowing you to process proteomics and metabolomics workflows without writing C++ code.
Proteomics workflows support reading and writing major MS data formats including mzML, mzXML, FASTA, mzIdentML, mzTab, pepXML, and protXML for file I/O operations.
Feature detection and peptide identification are performed by manipulating Pythonic data structures like MSExperiment, FeatureMap, and PeptideIdentification to build end-to-end MS data workflows.
Metabolomics data pipelines can be built entirely in Python using OpenMS bindings, covering file I/O, feature detection, identification, and quantification for comprehensive analysis.
Python bindings for MS data analysis expose core OpenMS capabilities but may introduce overhead compared to native C++ execution, requiring Python environment management for large-scale proteomics or metabolomics datasets.
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