matchms

To identify unknown metabolites from mass spectrometry data, you can match query spectra against reference libraries using automated spectral matching. This process compares your experimental mass spectra to known references for high-throughput compound annotation.

For spectral matching, you can import and export mass spectrometry data in standard formats including MGF, MSP, and GNPS JSON. These formats allow you to load reference libraries and query spectra to identify unknown compounds.

For mass spectrometry library search, you can compute similarities using CosineGreedy, ModifiedCosine, NeutralLossesCosine, or FingerprintSimilarity metrics. These diverse spectral and structural similarity calculations support accurate compound identification.

Yes, you can build reproducible analysis pipelines for high-throughput metabolomics workflows. These pipelines perform large-scale library searches to annotate unknown spectra and enrich them with chemical information to support downstream interpretation.

Yes, spectral matching supports comparative metabolomics studies. Use cases include metabolite identification, library searching, quality control, and comparative studies by applying flexible processing pipelines across your mass spectrometry data.

Automated spectral matching accelerates compound identification by replacing manual, error-prone library comparisons. It provides a reproducible framework to match mass spectra against reference libraries, ensuring high-throughput and accurate annotation of unknowns.