Parse LC-MS/MS spectral files and generate quantification-ready feature maps.
npx skills add https://github.com/shushuzn/Rairos --skill pyopenms-shushuzn
Please help me install this Agent Skill. Skill: pyopenms Source: https://github.com/shushuzn/Rairos/tree/main/skills/pyopenms Command: npx skills add https://github.com/shushuzn/Rairos --skill pyopenms-shushuzn
PyOpenMS eliminates the manual, error-prone effort of processing LC-MS/MS data by providing end-to-end Python access to established mass spectrometry workflows, from file handling to feature detection, identification, and quantification.
Use PyOpenMS to load sample.mzML and print the number of spectra after parsing the experiment from the file.
High-intent search queries and answers about installing and using this skill.
Parse raw LC-MS/MS spectral files in Python by loading mzML or mzXML formats into MSExperiment data structures. This reads the raw spectral data and outputs quantification-ready feature maps for downstream proteomics or metabolomics identification.
Detect chromatographic features and link them across replicate samples to build consensus feature maps. This links features into ConsensusMap structures, producing linked quantitative data ready for downstream statistical analysis.
Apply FDR filtering to peptide and protein identification results by loading idXML or mzIdentML files. This processes identification data through post-processing steps, outputting filtered protein inference results for confident proteomics identification.
Untargeted metabolomics pipelines with adduct handling are supported for small molecule feature detection. This runs preprocessing tuned for metabolomics, outputting adduct-aware feature maps ready for downstream small molecule quantification.
Signal preprocessing steps for mass spectrometry data include Gaussian smoothing, peak picking, and centroiding. These preprocessing actions normalize raw spectral signals, producing cleaned data structures required before accurate feature detection.
Convert mzML files to featureXML for proteomics quantification by parsing the raw experiment and applying feature detection. This transforms raw spectral data into feature maps, yielding quantification-ready featureXML outputs for downstream statistics.
OwnLabAIAnalyze LC-MS data with Python bindings for OpenMS.
hxk622Analyze mass spectrometry data for proteomics and metabolomics workflows.
dailycafiProcess LC-MS mzML datasets for peak picking, feature detection, and format conversion.
inflexa-aiProcess mass spectrometry data with XCMS and matchms for metabolomics analysis.
