Compute cosine-based spectral similarity scores for LC-MS/MS metabolomics library matching.
npx skills add https://github.com/shushuzn/Rairos --skill matchms-shushuzn
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/shushuzn/Rairos/tree/main/skills/matchms Command: npx skills add https://github.com/shushuzn/Rairos --skill matchms-shushuzn
Matchms helps you compare mass spectra to find the most likely compounds by scoring spectral similarity, cleaning and harmonizing metadata, and searching against reference spectral libraries.
Use the matchms skill to identify unknown compounds by matching spectra from a query file against a reference library using cosine similarity after applying default filters and normalization.
High-intent search queries and answers about installing and using this skill.
To identify metabolites through mass spectra matching, load your unknown query spectra and a reference library, then compute cosine or modified-cosine similarity scores to rank candidate compounds. The workflow requires importing spectral data, harmonizing metadata, and normalizing intensities before calculating precursor-aware similarity scores for downstream reporting.
For metabolomics spectral matching, you can import spectra from mzML, mzXML, MGF, MSP, and JSON (GNPS) formats. After processing and matching, you can export the results back into shareable formats for downstream analysis and reporting.
To calculate cosine similarity scores for LC-MS/MS spectral matching, load your spectra, run metadata harmonization and intensity normalization, and ensure valid precursor metadata for precursor-aware scoring. The process computes cosine, modified-cosine, and neutral-loss similarity scores to rank the best candidate matches.
Before searching mass spectral libraries, required peak preprocessing steps include applying default metadata harmonization, normalizing intensities, filtering peaks, and enforcing quality thresholds. These steps ensure valid precursor metadata required for accurate precursor-aware similarity scoring.
Yes, you can run end-to-end large-scale library search workflows for mass spectrometry data. The process handles importing common spectral formats, applying preprocessing filters, and computing similarity scores to rank candidate matches across extensive reference libraries.
The best way to clean and harmonize metadata for mass spectra before compound identification is applying default harmonization filters, normalizing intensities, and enforcing quality thresholds. This ensures valid precursor metadata required for accurate precursor-aware similarity scoring during spectral matching.
silversteinStandardize mass spectrometry spectra and compute similarity scores for compound identification.
dailycafiMatch MS/MS spectra against reference libraries to identify compounds.
tassiovaleCompare mass spectra and identify compounds using spectral libraries.
swaruplabRank candidate metabolites by comparing mass spectra with matchs cosine metrics.
