Learning representations of microbe–metabolite interactions

James T. Morton, Alexander A. Aksenov, Louis Felix Nothias, James R. Foulds, Robert A. Quinn, Michelle H. Badri, Tami L. Swenson, Marc W. Van Goethem, Trent R. Northen, Yoshiki Vazquez-Baeza, Mingxun Wang, Nicholas A. Bokulich, Aaron Watters, Se Jin Song, Richard Bonneau, Pieter C. Dorrestein, Rob Knight

Research output: Contribution to journalArticle

Abstract

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe–metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Original languageEnglish (US)
Pages (from-to)1306-1314
Number of pages9
JournalNature methods
Volume16
Issue number12
DOIs
StatePublished - Dec 1 2019

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ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Cite this

Morton, J. T., Aksenov, A. A., Nothias, L. F., Foulds, J. R., Quinn, R. A., Badri, M. H., Swenson, T. L., Van Goethem, M. W., Northen, T. R., Vazquez-Baeza, Y., Wang, M., Bokulich, N. A., Watters, A., Song, S. J., Bonneau, R., Dorrestein, P. C., & Knight, R. (2019). Learning representations of microbe–metabolite interactions. Nature methods, 16(12), 1306-1314. https://doi.org/10.1038/s41592-019-0616-3