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Title: Learning representations of microbe–metabolite interactions

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.

Authors:
ORCiD logo; ORCiD logo; ; ; ; ; ; ; ORCiD logo; ; ; ; ; ORCiD logo; ; ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1582030
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Nature Methods
Additional Journal Information:
Journal Volume: 16; Journal Issue: 12; Journal ID: ISSN 1548-7091
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English

Citation Formats

Morton, James T., Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A., Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., and Knight, Rob. Learning representations of microbe–metabolite interactions. United States: N. p., 2019. Web. doi:10.1038/s41592-019-0616-3.
Morton, James T., Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A., Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., & Knight, Rob. Learning representations of microbe–metabolite interactions. United States. doi:10.1038/s41592-019-0616-3.
Morton, James T., Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A., Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., and Knight, Rob. Mon . "Learning representations of microbe–metabolite interactions". United States. doi:10.1038/s41592-019-0616-3.
@article{osti_1582030,
title = {Learning representations of microbe–metabolite interactions},
author = {Morton, James T. and Aksenov, Alexander A. and Nothias, Louis Felix and Foulds, James R. and Quinn, Robert A. and Badri, Michelle H. and Swenson, Tami L. and Van Goethem, Marc W. and Northen, Trent R. and Vazquez-Baeza, Yoshiki and Wang, Mingxun and Bokulich, Nicholas A. and Watters, Aaron and Song, Se Jin and Bonneau, Richard and Dorrestein, Pieter C. and Knight, Rob},
abstractNote = {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.},
doi = {10.1038/s41592-019-0616-3},
journal = {Nature Methods},
number = 12,
volume = 16,
place = {United States},
year = {2019},
month = {11}
}

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