matchms

To match unknown mass spectrometry spectra against a reference library, you import query and reference data, apply preprocessing like metadata harmonization and peak filtering, then compute cosine or modified-cosine similarity scores to retrieve top candidate matches.

Yes, you can import and export mzML, mzXML, MGF, MSP, JSON, and USI formats for metabolomics analysis. This allows you to load raw spectral data, process it, and save the cleaned outputs back into your required MS data formats.

Spectral similarity scoring compares normalized query spectra to reference libraries using cosine-based and precursor-aware scoring, including neutral-loss comparisons, to accurately rank and identify compounds in metabolomics workflows.

You normalize peak intensities and filter low-quality MS spectra by applying preprocessing constraints that remove precursor-region peaks, harmonize metadata, and enforce minimum peak counts to ensure consistent, reproducible spectral data.

Yes, precursor-aware scoring works with LC-MS/MS data by utilizing neutral-loss comparisons and structural fingerprint similarity alongside modified-cosine scoring to improve compound identification accuracy during library matching.