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Curr Opin Microbiol. 2015 Jun;25:56-66. doi: 10.1016/j.mib.2015.04.004. Epub 2015 May 22.

Metagenomics meets time series analysis: unraveling microbial community dynamics.

Author information

1
Department of Microbiology and Immunology, Rega Institute KU Leuven, Leuven, Belgium; VIB Center for the Biology of Disease, VIB, Belgium; Laboratory of Microbiology, Vrije Universiteit Brussel (VUB), Brussels, Belgium. Electronic address: karoline.faust@vib-kuleuven.be.
2
Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands; Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland.
3
Unité de Chronobiologie Théorique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Brussels, Belgium; Interuniversity Institute of Bioinformatics in Brussels (IB)(2), ULB-VUB, Brussels, Belgium.
4
Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands; Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland; Immunobiology Research Program, Department of Bacteriology and Immunology, Haartman Institute, University of Helsinki, Helsinki, Finland.
5
Department of Microbiology and Immunology, Rega Institute KU Leuven, Leuven, Belgium; VIB Center for the Biology of Disease, VIB, Belgium; Laboratory of Microbiology, Vrije Universiteit Brussel (VUB), Brussels, Belgium. Electronic address: jeroen.raes@vib-kuleuven.be.

Abstract

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. Here, we apply these techniques on multiple longitudinal datasets to illustrate their potential for microbiome research.

PMID:
26005845
DOI:
10.1016/j.mib.2015.04.004
[Indexed for MEDLINE]
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