Analyze proteomics and metabolomics mass spectrometry data with Python bindings.
npx skills add https://github.com/Yezez9/Research-Agent --skill pyopenms-yezez9
Please help me install this Agent Skill. Skill: pyopenms Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/pyopenms Command: npx skills add https://github.com/Yezez9/Research-Agent --skill pyopenms-yezez9
This Skill provides a comprehensive platform for mass spectrometry data analysis, enabling researchers to process complex proteomics and metabolomics datasets efficiently.
Use the pyopenms skill to load the mzML file 'sample.mzML' and print the number of spectra.
High-intent search queries and answers about installing and using this skill.
To analyze raw LC-MS/MS data for proteomics in Python, you can use this Skill to load mzML files and run peptide identification workflows. It provides comprehensive algorithms for identifying and quantifying thousands of proteins in biological samples.
Processing untargeted metabolomics data to discover biomarkers requires identifying and grouping chromatographic features across samples. This Skill supports complete metabolomics workflows including signal processing, feature detection, and linking for quantitative analysis.
Yes, you can read and export mzML mass spectrometry files using Python. This Skill supports numerous mass spectrometry file formats including mzML and mzXML, enabling efficient data import and export for downstream computational analysis.
To perform signal smoothing and centroiding on spectral data, this Skill provides dedicated signal processing tools. These functions allow you to filter and refine raw mass spectrometry data before moving on to feature detection.
Yes, this platform supports false discovery rate (FDR) control for protein identification. It facilitates complete identification workflows, allowing you to confidently validate peptide and protein identification results from your mass spectrometry data.
You need feature detection and linking in mass spectrometry analysis when performing quantitative comparisons across multiple samples. This process identifies chromatographic features and groups them, which is essential for accurately quantifying proteins or metabolites.
jimmc414Analyze mass spectrometry data using Python bindings for the OpenMS library.
Lord1EgyptAnalyze mass spectrometry data for proteomics and metabolomics workflows.
dailycafiIntegrate multi-omics datasets with MOFA2 to discover shared and modality-specific latent factors.
SologaProcess LC-MS/MS proteomics and metabolomics data through a Python interface to OpenMS.
