Compare mass spectra to reference libraries using multiple similarity metrics.
npx skills add https://github.com/mkurman/tamux --skill matchms-mkurman
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/matchms Command: npx skills add https://github.com/mkurman/tamux --skill matchms-mkurman
Mass spectrometry data analysis requires reliable methods to compare spectra and identify compounds across libraries.
Load a spectral library and a set of unknown spectra, then run a CosineGreedy comparison to obtain top matches.
High-intent search queries and answers about installing and using this skill.
Spectral matching identifies unknown metabolites by comparing unknown mass spectrometry spectra against reference libraries using similarity scoring. Matchms supports this library-search workflow by importing spectra, harmonizing metadata, and computing similarity scores like CosineGreedy to return top compound matches.
Spectral similarity scoring in matchms offers multiple metrics for compound identification. CosineGreedy provides standard cosine similarity, while ModifiedCosine allows shifted peak matching to tolerate precursor ion differences. Additional options include CosineHungarian and NeutralLossesCosine for varied structural comparison.
Compound identification requires a Python environment with numpy. Optional chemistry tools like rdkit are needed to enable fingerprinting and structure-aware features, but basic spectral matching and similarity scoring can function without them.
Reproducible metabolomics pipelines are built by combining metadata processing, peak filtering, and annotation steps. Matchms enables large-scale spectral library searching by harmonizing metadata and applying similarity scoring across unknown spectra and reference libraries.
Cross-study comparison of mass spectrometry spectra requires harmonizing metadata and applying consistent similarity metrics. Matchms enables this by importing standard spectral formats, normalizing metadata, and computing similarity scores like NeutralLossesCosine across datasets.
Metabolite identification relies on comparing unknown spectra to reference libraries to find matches. Without a reference library, matchms can still harmonize spectral data and compute similarity between unknown spectra, but compound identification requires known reference spectra.
robinbarvaagProcess mass spectrometry spectra and calculate similarity scores with matchms.
dailycafiProcess GC-MS raw data into deconvolved spectra and library-matched compound annotations.
Hung-3008Match mass spectra against reference libraries to identify compounds.
sanand0Process and analyze mass spectrometry data with spectral matching and similarity scoring.
