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Enhancing untargeted metabolomics using metadata-based source annotation.

Julia M GauglitzKiana A WestWout BittremieuxCandace L WilliamsKelly C WeldonMorgan PanitchpakdiFrancesca Di OttavioChristine M AcevesElizabeth BrownNicole C SikoraAlan K JarmuschCameron MartinoAnupriya TripathiMichael J MeehanKathleen DorresteinJustin P ShafferRoxana CorasFernando VargasLindsay DeRight GoldasichTara SchwartzMacKenzie BryantGregory HumphreyAbigail J JohnsonKatharina SpenglerPedro Belda FerreEdgar DiazDaniel McDonaldQiyun ZhuEmmanuel O ElijahMingxun WangClarisse MarotzKate E SprecherDaniela Vargas-RoblesDana WithrowGail AckermannLourdes HerreraBarry J BradfordLucas Maciel Mauriz MarquesJuliano Geraldo AmaralRodrigo Moreira da SilvaFlavio Protasio VerasThiago Mattar CunhaRene Donizeti Ribeiro OliveiraPaulo Louzada-JuniorRobert H MillsPaulina K PiotrowskiStephanie L ServetasSandra M Da SilvaChristina M JonesNancy J LinKatrice A LippaScott A JacksonRima Kaddurah DaoukDouglas GalaskoParambir S DulaiTatyana I KalashnikovaCurt WittenbergRobert TerkeltaubMegan M DotyJae H KimDavid J GonzalezJulia Beauchamp-WaltersKenneth P WrightMaria Gloria Dominguez BelloMark ManaryMichelli F OliveiraBrigid S BolandNorberto Peporine LopesMonica GumaAustin D SwaffordRachel J DuttonRob KnightPieter C Dorrestein
Published in: Nature biotechnology (2022)
Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.
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