{"doi":"10.1101/2021.01.06.425569","title":"Metabolite discovery through global annotation of untargeted metabolomics data","abstract":"Abstract Liquid chromatography-high resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantitate all metabolites, but most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times, and (when available) MS/MS fragmentation patterns. Peaks are connected based on mass differences reflecting adducting, fragmentation, isotopes, or feasible biochemical transformations. 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 MS/MS spectra. Applying this approach to yeast and mouse data, we identified five novel metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to annotate untargeted metabolomics data, revealing novel metabolites.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":213741,"datarank":0.41588830833596724,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.0,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7437,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":235559,"name":"Wenyun Lu","orcid":"0000-0003-1787-2617","position":1,"is_corresponding":false},{"id":235558,"name":"Lin Wang","orcid":"0000-0002-9370-6891","position":2,"is_corresponding":false},{"id":578848,"name":"Xi Xing","orcid":null,"position":3,"is_corresponding":false},{"id":621099,"name":"Ziyang Chen","orcid":"0000-0002-2467-8372","position":4,"is_corresponding":false},{"id":621100,"name":"Xin Teng","orcid":"0000-0002-8354-0090","position":5,"is_corresponding":false},{"id":235553,"name":"Xianfeng Zeng","orcid":"0000-0001-9593-3388","position":6,"is_corresponding":false},{"id":621101,"name":"Antonio D. Muscarella","orcid":"0000-0001-8399-4378","position":7,"is_corresponding":false},{"id":621102,"name":"Yihui Shen","orcid":"0000-0001-8490-6413","position":8,"is_corresponding":false},{"id":225542,"name":"Alexis J. Cowan","orcid":"0000-0002-5365-6789","position":9,"is_corresponding":false},{"id":244440,"name":"Melanie R. McReynolds","orcid":"0000-0001-5427-2739","position":10,"is_corresponding":false},{"id":621103,"name":"Brandon J. Kennedy","orcid":"0000-0003-1892-8926","position":11,"is_corresponding":false},{"id":622406,"name":"Ashley M. Lato","orcid":null,"position":12,"is_corresponding":false},{"id":621104,"name":"Shawn R. Campagna","orcid":"0000-0001-6809-3862","position":13,"is_corresponding":false},{"id":317101,"name":"Mona Singh","orcid":"0000-0001-8271-6026","position":14,"is_corresponding":false},{"id":108672,"name":"Joshua D. Rabinowitz","orcid":"0000-0002-1247-4727","position":15,"is_corresponding":false},{"id":578389,"name":"Li Chen","orcid":"0000-0001-8685-3466","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:52:41.672172Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}