Integrating Computational Methods to Investigate the Macroecology of Microbiomes.

Journal: Frontiers in genetics
Published Date:

Abstract

Studies in microbiology have long been mostly restricted to small spatial scales. However, recent technological advances, such as new sequencing methodologies, have ushered an era of large-scale sequencing of environmental DNA data from multiple biomes worldwide. These global datasets can now be used to explore long standing questions of microbial ecology. New methodological approaches and concepts are being developed to study such large-scale patterns in microbial communities, resulting in new perspectives that represent a significant advances for both microbiology and macroecology. Here, we identify and review important conceptual, computational, and methodological challenges and opportunities in microbial macroecology. Specifically, we discuss the challenges of handling and analyzing large amounts of microbiome data to understand taxa distribution and co-occurrence patterns. We also discuss approaches for modeling microbial communities based on environmental data, including information on biological interactions to make full use of available Big Data. Finally, we summarize the methods presented in a general approach aimed to aid microbiologists in addressing fundamental questions in microbial macroecology, including classical propositions (such as "everything is everywhere, but the environment selects") as well as applied ecological problems, such as those posed by human induced global environmental changes.

Authors

  • Rilquer Mascarenhas
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Flávia M Ruziska
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Eduardo Freitas Moreira
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Amanda B Campos
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Miguel Loiola
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Kaike Reis
    Chemical Engineering Department, Polytechnic School of Federal University of Bahia, Salvador, Brazil.
  • Amaro E Trindade-Silva
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Felipe A S Barbosa
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Lucas Salles
    Institute of Geology, Federal University of Bahia, Salvador, Brazil.
  • Rafael Menezes
    Department of Ecology, Biosciences Institute, University of Sao Paulo, Sao Paulo, Brazil.
  • Rafael Veiga
    Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Oswaldo Cruz, Brazil.
  • Felipe H Coutinho
    Evolutionary Genomics Group, Departamento de Producción Vegetal y Microbiología, Universidad Miguel Hernández de Elche, San Juan de Alicante, Spain.
  • Bas E Dutilh
    Theoretical Biology and Bioinformatics, Utrecht University, Utrecht, Netherlands.
  • Paulo R Guimarães
    Department of Ecology, Biosciences Institute, University of Sao Paulo, Butantã, Brazil.
  • Ana Paula A Assis
    Department of Ecology, Biosciences Institute, University of Sao Paulo, Butantã, Brazil.
  • Anderson Ara
    Institute of Mathematics, Federal University of Bahia, Salvador, Brazil.
  • José G V Miranda
    Institute of Physics, Federal University of Bahia, Salvador, Brazil.
  • Roberto F S Andrade
    Institute of Physics, Federal University of Bahia, Salvador, Brazil.
  • Bruno Vilela
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.
  • Pedro Milet Meirelles
    Institute of Biology, Federal University of Bahia, Salvador, Brazil.

Keywords

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