Compute cosine and modified cosine similarity scores for mass spectra in metabolomics.
npx skills add https://github.com/MAF2414/kyco --skill matchms-maf2414
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/MAF2414/kyco/tree/main/.claude/skills/matchms Command: npx skills add https://github.com/MAF2414/kyco --skill matchms-maf2414
Mass spectrometry workflows often require robust, reproducible processing, comparison, and identification of spectra across libraries. Matchms provides a Python toolkit to import, standardize, filter, and compare spectra, enabling reliable compound identification from spectral data.
Process a small example dataset by loading spectra, applying default filters, and computing cosine similarity against a reference spectrum.
High-intent search queries and answers about installing and using this skill.
Spectral matching identifies compounds by computing similarity scores between your mass spectra and reference libraries. This Skill applies cosine and modified cosine similarity metrics to match unknown spectra against known standards for compound identification.
You can import mass spectrometry data in MGF, mzML, and JSON formats for spectral matching. The toolkit standardizes imported spectra through metadata harmonization and filtering before computing similarity scores against reference libraries.
Cosine similarity matches mass spectra based on shared mass-to-charge ratios and intensities, while modified cosine similarity accommodates precursor mass differences. This Skill supports both metrics, alongside neutral losses cosine, for flexible compound identification.
Yes, spectral matching for metabolomics workflows requires Python and the matchms library to function. It leverages Python to import, filter, and compare mass spectra against reference libraries for compound identification.
Build reproducible pipelines by loading spectra from MGF or mzML files, applying default quality control filters for metadata harmonization, and computing similarity scores against reference libraries to derive chemical information.
Neutral losses cosine similarity is needed when identifying compounds based on characteristic mass losses rather than direct fragment matches. This Skill includes this metric alongside standard and modified cosine methods for comprehensive library matching.
silversteinStandardize mass spectrometry spectra and compute similarity scores for compound identification.
RamanEbrahimiCompare mass spectra and identify compounds from spectral libraries.
TianGzlabAnnotate metabolomics features by matching MS2 spectra against SIRIUS, GNPS, and MetFrag databases.
tassiovaleCompare mass spectra and identify compounds using spectral libraries.
