Process mass spectrometry data into feature maps and quantification tables.
npx skills add https://github.com/dralkh/seerai --skill pyopenms-dralkh
Please help me install this Agent Skill. Skill: pyopenms Source: https://github.com/dralkh/seerai/tree/main/skills/pyopenms Command: npx skills add https://github.com/dralkh/seerai --skill pyopenms-dralkh
It removes the manual burden of processing LC-MS data by helping you inspect files, preprocess spectra, detect features, quantify samples, and interpret identifications in one workflow.
Use the pyopenms skill to analyze the attached LC-MS data file and produce a feature table or identification summary with the appropriate workflow for your experiment.
High-intent search queries and answers about installing and using this skill.
Feature detection from mzML files involves signal preprocessing, peak picking, and linking features across runs into a consensus map. This produces a consensus quantification table ready for downstream statistical analysis.
Proteomics workflows support in-silico peptide digestion from FASTA files and theoretical spectra generation. These functions predict peptide fragments to match against experimental mass spectrometry data for accurate protein identification.
Mass spectrometry data export to GNPS or SIRIUS is fully supported. This enables downstream molecular networking and compound formula annotation directly from your processed metabolomics feature maps.
Required signal processing steps before feature detection include smoothing, centroiding, normalization, and filtering raw spectra. These steps reduce noise and ensure accurate peak picking for metabolomics and proteomics analysis.
Adduct annotation and accurate-mass searches group detected features by mass differences and match them against known adduct patterns. This provides accurate-mass annotations for interpreting metabolomics feature maps.
pyopenms supports mzXML, mzML, featureXML, consensusXML, idXML, mzTab, and FASTA formats. It processes raw mass spectrometry data through feature detection and consensus quantification to final identification summaries.
dailycafiConvert vendor-specific mass spectrometry files to mzML and mzXML formats.
TianGzlabAnnotate metabolomics features by matching MS2 spectra against SIRIUS, GNPS, and MetFrag databases.
youyinnnCreate, manipulate, and analyze complex networks in Python.
jackspaceProvide a Python interface to OpenMS for mass spectrometry data analysis.
