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BugBase predicts organism-level microbiome phenotypes

Tonya Ward, Jake Larson, Jeremy Meulemans, Ben Hillmann, Joshua Lynch, Dimitri Sidiropoulos, John R. Spear, Greg Caporaso, Ran Blekhman, Rob Knight, Ryan Fink, Dan Knights
doi: https://doi.org/10.1101/133462
Tonya Ward
Biotechnology Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA
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Jake Larson
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
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Jeremy Meulemans
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
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Ben Hillmann
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
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Joshua Lynch
Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota 55455, USA
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Dimitri Sidiropoulos
Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota 55455, USA
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John R. Spear
Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado 80401, USA
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Greg Caporaso
Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USACenter for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, Arizona, USA
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Ran Blekhman
Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota 55455, USADepartment of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, Minnesota 55108, USA
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Rob Knight
Department of Computer of Science, University of California San Diego, San Diego, California, USADepartment of Pediatrics, University of California San Diego, San Diego, California, USACenter for Microbiome Innovation, University of California San Diego, San Diego, California, USA
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Ryan Fink
Department of Biology, St Cloud State University, St. Cloud, Minnesota 56301, USAFood Science and Nutrition, University of Minnesota, Saint Paul, Minnesota 55108, USA
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Dan Knights
Biotechnology Institute, University of Minnesota, Minneapolis, Minnesota 55455, USADepartment of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
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Abstract

Shotgun metagenomics and marker gene amplicon sequencing can be used to directly measure or predict the functional repertoire of the microbiota en masse, but current methods do not readily estimate the functional capability of individual microorganisms. Here we present BugBase, an algorithm that predicts organism-level coverage of functional pathways as well as biologically interpretable phenotypes such as oxygen tolerance, Gram staining and pathogenic potential, within complex microbiomes using either whole-genome shotgun or marker gene sequencing data. We find BugBase’s organism-level pathway coverage predictions to be statistically higher powered than current ‘bag-of-genes’ approaches for discerning functional changes in both host-associated and environmental microbiomes.

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Posted May 02, 2017.
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BugBase predicts organism-level microbiome phenotypes
Tonya Ward, Jake Larson, Jeremy Meulemans, Ben Hillmann, Joshua Lynch, Dimitri Sidiropoulos, John R. Spear, Greg Caporaso, Ran Blekhman, Rob Knight, Ryan Fink, Dan Knights
bioRxiv 133462; doi: https://doi.org/10.1101/133462
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BugBase predicts organism-level microbiome phenotypes
Tonya Ward, Jake Larson, Jeremy Meulemans, Ben Hillmann, Joshua Lynch, Dimitri Sidiropoulos, John R. Spear, Greg Caporaso, Ran Blekhman, Rob Knight, Ryan Fink, Dan Knights
bioRxiv 133462; doi: https://doi.org/10.1101/133462

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