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Enhancing untargeted metabolomics using metadata-based source annotation

by Williams, Candace L. , Rhee, Kyung E. , Zhu, Qiyun , Jones, Christina M. , Lopes, Norberto Peporine , Elijah, Emmanuel O. , Gauglitz, Julia M. , Cunha, Thiago Mattar , West, Kiana A. , Knight, Rob , Meehan, Michael J. , Tripathi, Anupriya , Dutton, Rachel J. , Bittremieux, Wout , Shaffer, Justin P. , Lippa, Katrice A. , Wright, Kenneth P. , Bradford, Barry J. , Oliveira, Michelli F. , Wittenberg, Curt , Guma, Monica , Brown, Elizabeth , Belda-Ferre, Pedro , Oliveira, Rene Donizeti Ribeiro , Mills, Robert H. , Herrera, Lourdes , Boland, Brigid S. , Diaz, Edgar , Swafford, Austin D. , Dorrestein, Pieter C. , Bryant, MacKenzie , Louzada-Junior, Paulo , Vargas-Robles, Daniela , Martino, Cameron , Marotz, Clarisse , Daouk, Rima Kaddurah , Jarmusch, Alan K. , Spengler, Katharina , Coras, Roxana , Ackermann, Gail , Doty, Megan M. , Jackson, Scott A. , Dominguez-Bello, Maria Gloria , Sikora, Nicole C. , Vargas, Fernando , Sprecher, Kate E. , Kalashnikova, Tatyana I. , Dorrestein, Kathleen , McDonald, Daniel , Beauchamp-Walters, Julia , Da Silva, Sandra M. , Withrow, Dana , Humphrey, Gregory , Silva, Rodrigo Moreira , Dulai, Parambir S. , Veras, Flavio Pro

in 631/114/129 / 631/114/1314 / 631/1647/296 / 631/1647/320 / Agriculture / Annotations / Bioinformatics / Biomedical and Life Sciences / Biomedical Engineering/Biotechnology / Biomedicine / Biotechnology / Brief Communication / Caffeine / Chromatography / Data science / Datasets / Diet / Empirical analysis / Food / Food sources / Humans / Libraries / Life Sciences / Mass spectrometry / Mass spectroscopy / Metabolomics / Metabolomics - methods / Metadata / Ontology / Scientific imaging / Tandem Mass Spectrometry / Vocabularies & taxonomies

2022

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Enhancing untargeted metabolomics using metadata-based source annotation

2022

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2022

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Journal Article

Enhancing untargeted metabolomics using metadata-based source annotation

Williams, Candace L.,

Rhee, Kyung E.,

Zhu, Qiyun,

Jones, Christina M.,

Lopes, Norberto Peporine,

Elijah, Emmanuel O.,

Gauglitz, Julia M.,

Cunha, Thiago Mattar,

West, Kiana A.,

Knight, Rob,

Meehan, Michael J.,

Tripathi, Anupriya,

Dutton, Rachel J.,

Bittremieux, Wout,

Shaffer, Justin P.,

Lippa, Katrice A.,

Wright, Kenneth P.,

Bradford, Barry J.,

Oliveira, Michelli F.,

Wittenberg, Curt,

Guma, Monica,

Brown, Elizabeth,

Belda-Ferre, Pedro,

Oliveira, Rene Donizeti Ribeiro,

Mills, Robert H.,

Herrera, Lourdes,

Boland, Brigid S.,

Diaz, Edgar,

Swafford, Austin D.,

Dorrestein, Pieter C.,

Bryant, MacKenzie,

Louzada-Junior, Paulo,

Vargas-Robles, Daniela,

Martino, Cameron,

Marotz, Clarisse,

Daouk, Rima Kaddurah,

Jarmusch, Alan K.,

Spengler, Katharina,

Coras, Roxana,

Ackermann, Gail,

Doty, Megan M.,

Jackson, Scott A.,

Dominguez-Bello, Maria Gloria,

Sikora, Nicole C.,

Vargas, Fernando,

Sprecher, Kate E.,

Kalashnikova, Tatyana I.,

Dorrestein, Kathleen,

McDonald, Daniel,

Beauchamp-Walters, Julia,

Da Silva, Sandra M.,

Withrow, Dana,

Humphrey, Gregory,

Silva, Rodrigo Moreira,

Dulai, Parambir S.,

Veras, Flavio Pro

2022

Overview

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. Metabolomics is improved by using a reference library of both known and unknown molecules.

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Nature Publishing Group US,Nature Publishing Group

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