Enhancing untargeted metabolomics using metadata-based source annotation.
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ABSTRACT:
SUBMITTER: Gauglitz JM
PROVIDER: S-EPMC10277029 | biostudies-literature | 2022 Dec
REPOSITORIES: biostudies-literature
Publications
Enhancing untargeted metabolomics using metadata-based source annotation.
Gauglitz Julia M JM West Kiana A KA Bittremieux Wout W Williams Candace L CL Weldon Kelly C KC Panitchpakdi Morgan M Di Ottavio Francesca F Aceves Christine M CM Brown Elizabeth E Sikora Nicole C NC Jarmusch Alan K AK Martino Cameron C Tripathi Anupriya A Meehan Michael J MJ Dorrestein Kathleen K Shaffer Justin P JP Coras Roxana R Vargas Fernando F Goldasich Lindsay DeRight LD Schwartz Tara T Bryant MacKenzie M Humphrey Gregory G Johnson Abigail J AJ Spengler Katharina K Belda-Ferre Pedro P Diaz Edgar E McDonald Daniel D Zhu Qiyun Q Elijah Emmanuel O EO Wang Mingxun M Marotz Clarisse C Sprecher Kate E KE Vargas-Robles Daniela D Withrow Dana D Ackermann Gail G Herrera Lourdes L Bradford Barry J BJ Marques Lucas Maciel Mauriz LMM Amaral Juliano Geraldo JG Silva Rodrigo Moreira RM Veras Flavio Protasio FP Cunha Thiago Mattar TM Oliveira Rene Donizeti Ribeiro RDR Louzada-Junior Paulo P Mills Robert H RH Piotrowski Paulina K PK Servetas Stephanie L SL Da Silva Sandra M SM Jones Christina M CM Lin Nancy J NJ Lippa Katrice A KA Jackson Scott A SA Daouk Rima Kaddurah RK Galasko Douglas D Dulai Parambir S PS Kalashnikova Tatyana I TI Wittenberg Curt C Terkeltaub Robert R Doty Megan M MM Kim Jae H JH Rhee Kyung E KE Beauchamp-Walters Julia J Wright Kenneth P KP Dominguez-Bello Maria Gloria MG Manary Mark M Oliveira Michelli F MF Boland Brigid S BS Lopes Norberto Peporine NP Guma Monica M Swafford Austin D AD Dutton Rachel J RJ Knight Rob R Dorrestein Pieter C PC
Nature biotechnology 20220707 12
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. ...[more]
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