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Enhancing untargeted metabolomics using metadata-based source annotation
- Gauglitz, Julia M;
- West, Kiana A;
- Bittremieux, Wout;
- Williams, Candace L;
- Weldon, Kelly C;
- Panitchpakdi, Morgan;
- Di Ottavio, Francesca;
- Aceves, Christine M;
- Brown, Elizabeth;
- Sikora, Nicole C;
- Jarmusch, Alan K;
- Martino, Cameron;
- Tripathi, Anupriya;
- Meehan, Michael J;
- Dorrestein, Kathleen;
- Shaffer, Justin P;
- Coras, Roxana;
- Vargas, Fernando;
- Goldasich, Lindsay DeRight;
- Schwartz, Tara;
- Bryant, MacKenzie;
- Humphrey, Gregory;
- Johnson, Abigail J;
- Spengler, Katharina;
- Belda-Ferre, Pedro;
- Diaz, Edgar;
- McDonald, Daniel;
- Zhu, Qiyun;
- Elijah, Emmanuel O;
- Wang, Mingxun;
- Marotz, Clarisse;
- Sprecher, Kate E;
- Vargas-Robles, Daniela;
- Withrow, Dana;
- Ackermann, Gail;
- Herrera, Lourdes;
- Bradford, Barry J;
- Marques, Lucas Maciel Mauriz;
- Amaral, Juliano Geraldo;
- Silva, Rodrigo Moreira;
- Veras, Flavio Protasio;
- Cunha, Thiago Mattar;
- Oliveira, Rene Donizeti Ribeiro;
- Louzada-Junior, Paulo;
- Mills, Robert H;
- Piotrowski, Paulina K;
- Servetas, Stephanie L;
- Da Silva, Sandra M;
- Jones, Christina M;
- Lin, Nancy J;
- Lippa, Katrice A;
- Jackson, Scott A;
- Daouk, Rima Kaddurah;
- Galasko, Douglas;
- Dulai, Parambir S;
- Kalashnikova, Tatyana I;
- Wittenberg, Curt;
- Terkeltaub, Robert;
- Doty, Megan M;
- Kim, Jae H;
- Rhee, Kyung E;
- Beauchamp-Walters, Julia;
- Wright, Kenneth P;
- Dominguez-Bello, Maria Gloria;
- Manary, Mark;
- Oliveira, Michelli F;
- Boland, Brigid S;
- Lopes, Norberto Peporine;
- Guma, Monica;
- Swafford, Austin D;
- Dutton, Rachel J;
- Knight, Rob;
- Dorrestein, Pieter C
- et al.
Published Web Location
https://doi.org/10.1038/s41587-022-01368-1Abstract
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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