Modeling time-series data from microbial communities

  • Benjamin J Ridenhour
    Department of Biological Sciences, University of Idaho , Moscow, ID, USA
  • Sarah L Brooker
    Bioinformatics and Computational Biology Program, University of Idaho , Moscow, ID, USA
  • Janet E Williams
    Bioinformatics and Computational Biology Program, University of Idaho , Moscow, ID, USA
  • James T Van Leuven
    Center for Modeling Complex Interactions, University of Idaho , Moscow, ID, USA
  • Aaron W Miller
    Department of Biology, University of Utah , Salt Lake City, UT, USA
  • M Denise Dearing
    Department of Biology, University of Utah , Salt Lake City, UT, USA
  • Christopher H Remien
    Bioinformatics and Computational Biology Program, University of Idaho , Moscow, ID, USA

書誌事項

公開日
2017-08-08
権利情報
  • https://academic.oup.com/pages/standard-publication-reuse-rights
  • http://www.springer.com/tdm
DOI
  • 10.1038/ismej.2017.107
公開者
Oxford University Press (OUP)

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説明

<jats:title>Abstract</jats:title> <jats:p>As sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (for example, skin or soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but methods to infer ecological interactions from these longitudinal data are limited. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that (1) takes advantage of the fact that the data are time series; (2) constrains estimates to allow for the possibility of many more interactions than data; and (3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.</jats:p>

収録刊行物

  • The ISME Journal

    The ISME Journal 11 (11), 2526-2537, 2017-08-08

    Oxford University Press (OUP)

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