matchms-spectral-matching

Mass spectrometry spectral matching identifies unknown metabolites by comparing query MS/MS spectra against curated reference libraries. By calculating similarity scores like modified cosine, it matches unknown compounds to known structures, providing names and confidence scores for biological samples.

Spectral matching supports importing, filtering, and normalizing mass spectrometry data from mzML, MGF, MSP, and JSON file formats. This allows you to process query MS/MS spectra and compare them against curated reference libraries for metabolite identification.

Available similarity scoring algorithms for MS/MS spectral matching include Cosine and Modified Cosine. These functions calculate similarity scores between query mass spectrometry spectra and reference libraries, enabling accurate and reproducible metabolite identification.

Yes, you can filter and normalize mass spectrometry data before spectral matching. The data processing capabilities support importing raw MS/MS spectra from formats like mzML and MSP, applying advanced filtering and normalization to ensure accurate metabolite identification.

Yes, matchms supports mzML and MSP files for metabolomics data processing, alongside MGF and JSON formats. It handles importing, filtering, and normalizing mass spectrometry data to prepare unknown MS/MS spectra for matching against reference libraries.