{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bilbao A"],"funding":["NIEHS NIH HHS","NIGMS NIH HHS"],"pagination":["2461"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10147702"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_abstract":["Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder fo"],"journal":["Nature communications"],"pubmed_title":["PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements."],"pmcid":["PMC10147702"],"funding_grant_id":["P42 ES027704","P41 GM103493","R01 GM141277"],"pubmed_authors":["Deng S","Pomraning KR","Burnet M","Smith RD","Kim J","Dai Z","Nicora CD","Kim YM","Fasani RA","Burnum-Johnson KE","Munoz N","Apffel A","Tsalenko A","Wilton R","Gee A","Michener JK","Oksen E","Magnuson JK","Petzold CJ","Gardner J","Poorey K","Baker ES","Orton DJ","Bilbao A","Gao Y","Weitz K","Tanjore D","Gladden JM"],"additional_accession":[]},"is_claimable":false,"name":"PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.","description":"Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder fo","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2026-04-30T12:32:38.971Z","creation":"2025-04-04T20:50:35.231Z"},"accession":"S-EPMC10147702","cross_references":{"pubmed":["37117207"],"doi":["10.1038/s41467-023-37031-9"]}}