Rank candidate metabolites by comparing mass spectra with matchs cosine metrics.
npx skills add https://github.com/swaruplab/operon --skill matchms-swaruplab
Please help me install this Agent Skill. Skill: matchms Source: https://github.com/swaruplab/operon/tree/main/src-tauri/protocols/matchms Command: npx skills add https://github.com/swaruplab/operon --skill matchms-swaruplab
Spectral similarity analysis in metabolomics is data-intensive and demands robust, repeatable comparison of mass spectra to identify compounds from libraries.
Load a reference library and a set of query spectra, apply default preprocessing, and run CosineGreedy similarity to obtain top matches.
High-intent search queries and answers about installing and using this skill.
Metabolite identification via spectral matching compares query mass spectra against reference libraries to rank candidates. This Skill computes similarity scores using CosineGreedy, ModifiedCosine, and NeutralLossesCosine algorithms to find the closest structural analogs.
Metabolomics library matching supports loading and comparing spectra across MGF, MSP, mzML, and JSON formats. You can apply default preprocessing to harmonize metadata before running similarity comparisons.
Yes, RDKit is required to generate structural fingerprints for compound identification. Leveraging fingerprint scoring alongside precursor-mz and metadata-based metrics builds robust multi-metric pipelines for ranking candidate metabolites.
CosineGreedy and ModifiedCosine are both spectral similarity algorithms for compound identification. ModifiedCosine accounts for neutral losses to detect structural analogs, whereas CosineGreedy provides standard fast similarity scoring for library matching.
Build a multi-metric identification pipeline by combining metadata-based, precursor-mz, and structural fingerprint scoring. This approach harmonizes spectra metadata and computes multiple similarity metrics to robustly rank candidate metabolites.
Yes, you can apply default preprocessing to harmonize metadata across MGF and MSP files before spectral matching. Metadata harmonization ensures consistent comparison when loading reference libraries and query spectra for compound identification.
OwnLabAIMatch mass spectra across libraries using multiple similarity metrics.
dailycafiMatch MS/MS spectra against reference libraries to identify compounds.
silversteinStandardize mass spectrometry spectra and compute similarity scores for compound identification.
MAF2414Compute cosine and modified cosine similarity scores for mass spectra in metabolomics.
