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Ramundi, V. ; Witting, M.

Tandem Mass Spectral Databases and Their Use in Non-Targeted Metabolomics.

In: Computational Methods and Data Analysis for Metabolomics. Berlin [u.a.]: Springer, 2026. 159-176 (Methods Mol. Biol. ; 3063)
DOI
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 MS2 libraries and ensure they work seamlessly together, we rely heavily on standardized file formats. Standard formats, such as mzML, MGF, MSP, JSON, and the MassBank format, each come with different levels of metadata richness and compatibility with various software tools. This chapter gives an overview of key MS2 libraries, discusses the strengths and weaknesses of standard data formats, and introduces R-based solutions for better integration.
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Publikationstyp Artikel: Sammelbandbeitrag/Buchkapitel
Schlagwörter Data Formats ; Mass Spectrometry ; Metabolite Identification ; Metabolomics ; Ms2 Libraries ; R ; Spectra Package
ISSN (print) / ISBN 1064-3745
e-ISSN 1940-6029
Bandtitel Computational Methods and Data Analysis for Metabolomics
Zeitschrift Methods in Molecular Biology
Quellenangaben Band: 3063, Heft: , Seiten: 159-176 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Core Facility Metabolomics and Proteomics (CF-MPC)