matchms

Spectral similarity analysis compares mass spectra to identify compounds by computing scores like CosineGreedy and ModifiedCosine. This skill automates the harmonization of spectral data and library matching for metabolomics research.

Spectral matching computes multiple similarity scores including CosineGreedy, CosineHungarian, ModifiedCosine, and NeutralLossesCosine. These metrics evaluate mass spectrometry data to determine the degree of similarity between unknown spectra and reference libraries.

Harmonizing mass spectrometry metadata requires importing raw spectral data, applying filters, and standardizing chemical annotations like InChIKey. This skill prepares spectra for robust comparison by building reproducible processing pipelines.

Processing large metabolomics datasets for compound identification is supported through Python-based tooling. This skill applies spectral similarity analysis and library matching across small to large datasets to identify unknown compounds.

Python-based tooling is required to build reproducible mass spectrometry workflows and harmonize spectral metadata. This skill leverages Python to compute similarity scores and enrich spectra with chemical information like fingerprints.

Enriching mass spectra with chemical information involves adding InChI, InChIKey, and fingerprints to support integrated workflows. This skill builds reproducible processing pipelines to annotate spectra and prepare them for compound identification.