Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification. A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online. XCMS Online: a web-based platform to process untargeted metabolomic data. NIST Standard Reference Database 1A (NIST, 2014) MassBank Europe High Quality Mass Spectral DataBase (MassBank) MassBank: a public repository for sharing mass spectral data for life sciences. Hydrogen rearrangement rules: computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. METLIN MS2 molecular standards database: a broad chemical and biological resource. ChemSpider: an online chemical information resource. KEGG as a reference resource for gene and protein annotation. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. HMDB 4.0: the human metabolome database for 2018. PubChem 2019 update: improved access to chemical data. Metabolomics: beyond biomarkers and towards mechanisms. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. A roadmap for natural product discovery based on large-scale genomics and metabolomics. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Durable remissions with ivosidenib in IDH1-mutated relapsed or refractory AML. The atom difference rule table is provided in Supplementary Data 1, the peak table for the yeast negative-mode data, as well as the NetID annotation results, putative metabolite list, and manual curation results are provided in Supplementary Data 2, an in-house retention time list for known metabolites is provided in Supplementary Data 3, the HMDB, YMDB, PubChemLite and PubChemLite_bio reference compound databases (customized to contain relevant information) are provided in Supplementary Data 4– 7, and MS2 spectra of newly discovered metabolites are provided in Supplementary Data 8.ĭiNardo, C. 1 is provided in GitHub ( ) and Zenodo ( ). R code for generating NetID statistics and for performing false discovery rate analysis in Fig. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.Īll LC-MS data, including the yeast and mouse metabolomics datasets, the 13C labeling datasets, and more than 2,000 targeted MS2 files collected from the liver data in mzXML format were deposited in MassIVE (ID no. Isotope tracer studies indicate active flux through these metabolites. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak–peak relationships, including for peaks lacking tandem mass spectrometry spectra. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified.
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