Provides a Python interface to OpenMS for mass spectrometry data analysis.
npx skills add https://github.com/yf8578/clawomics --skill pyopenms-yf8578
Please help me install this Agent Skill. Skill: pyopenms Source: https://github.com/yf8578/clawomics/tree/main/skills/pyopenms Command: npx skills add https://github.com/yf8578/clawomics --skill pyopenms-yf8578
This Skill provides a comprehensive platform for analyzing complex mass spectrometry data, enabling researchers to process raw spectra, identify peptides and proteins, and quantify metabolites.
Use the pyopenms skill to load the file 'sample.mzML' and print the number of spectra.
High-intent search queries and answers about installing and using this skill.
You can analyze mass spectrometry data in Python using this interface to perform file I/O, peptide identification, and quantitative analysis on standard formats like mzML and idXML.
The best way to process mzML files for metabolomics is using integrated signal processing workflows that detect chromatographic features, identify adducts, and annotate compounds directly from raw spectra.
Yes, you can identify and quantify proteins using integrated search engines with FDR control, then export the quantitative results for downstream statistical analysis.
Yes, it supports reading and writing standard mass spectrometry formats including mzML, mzXML, idXML, and featureXML to build robust bioinformatics pipelines.
You link chromatographic features by applying feature detection algorithms to raw mass spectrometry data, enabling accurate quantitation of peptides, proteins, and metabolites across samples.
dailycafiConvert vendor-specific mass spectrometry files to mzML and mzXML formats.
AlterLab-IEUProcess LC-MS/MS proteomics data with PyOpenMS for feature detection and identification.
TianGzlabPerform univariate statistical tests and FDR correction on metabolomics data.
Zeyuyang-0420Analyze mass spectrometry data with Python and OpenMS.
