Match LC-MS/MS query spectra against reference libraries using similarity metrics.
npx skills add https://github.com/JosephWoodall/noosphere --skill matchms-josephwoodall
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/matchms Command: npx skills add https://github.com/JosephWoodall/noosphere --skill matchms-josephwoodall
Identify compounds by spectral similarity matching between query spectra and reference libraries.
Load your spectral library and query data, then run a CosineGreedy-based search to retrieve top matches.
High-intent search queries and answers about installing and using this skill.
Spectral matching identifies unknown compounds by computing similarity between query spectra and reference libraries. You load your query data, run a similarity metric like CosineGreedy, and rank top hits to identify compounds across LC-MS/MS datasets.
Mass spectrometry library search supports CosineGreedy, CosineHungarian, ModifiedCosine, NeutralLossesCosine, and metadata-based FingerprintSimilarity metrics to compute spectral similarity and rank candidate matches.
Metabolomics data processing includes metadata harmonization to standardize precursor_mz, retention time, and inchikey during import, ensuring consistent spectral matching and accurate compound identification.
Yes, large-scale metabolomics library searches support in-context screening of unknowns by ranking candidate matches across LC-MS/MS datasets using multiple spectral similarity metrics.
NeutralLossesCosine and ModifiedCosine provide different spectral similarity calculations for metabolomics. Choose based on your dataset characteristics; both rank candidate matches during library searching to identify compounds.
jackspaceProcess MS/MS spectra with filtering, similarity scoring, and metadata harmonization pipelines.
shushuznCompute cosine-based spectral similarity scores for LC-MS/MS metabolomics library matching.
zongtingweiProcess LC-MS/GC-MS metabolomics data through preprocessing, normalization, annotation, statistics, and pathway mapping.
SologaCompare mass spectra using cosine and modified cosine similarity metrics.
