biostudies-literatureBilbao ANIEHS NIH HHSNIGMS NIH HHS2461https://www.ebi.ac.uk/biostudies/studies/S-EPMC10147702biostudies-literatureUnknown14(1)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 foNature communicationsPeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.PMC10147702P42 ES027704P41 GM103493R01 GM141277Deng SPomraning KRBurnet MSmith RDKim JDai ZNicora CDKim YMFasani RABurnum-Johnson KEMunoz NApffel ATsalenko AWilton RGee AMichener JKOksen EMagnuson JKPetzold CJGardner JPoorey KBaker ESOrton DJBilbao AGao YWeitz KTanjore DGladden JMfalsePeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.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 fo2023-01-01T00:00:00Z2023 Apr2026-04-30T12:32:38.971Z2025-04-04T20:50:35.231ZS-EPMC101477023711720710.1038/s41467-023-37031-9