ArticleMethods in molecular biology (Clifton, N.J.)2026
Tandem Mass Spectral Databases and Their Use in Non-Targeted Metabolomics.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based non-targeted metabolomics produces intricate datasets that need advanced tools for identifying metabolites (MetID). Metabolite annotation and identification in non-targeted metabolomics require high-quality fragmentation data from biological samples and reference libraries. Public and commercial databases, such as NIST, MassBank, MoNA, GNPS, HMDB, mzCloud, and METLIN, are vital resources for both spectral matching and providing training data for machine-learning-driven MetID tools. These libraries differ in their coverage, curation, and access models, and they are often supplemented by in-house databases that cater to specific laboratory conditions, ensuring the highest level of confidence in identifications. To maximize the benefits of MS
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