TY - JOUR
T1 - Methods for phylogenetic analysis of microbiome
AU - Washburne, Alex D.
AU - Morton, James T.
AU - Sanders, Jon
AU - McDonald, Daniel
AU - Zhu, Qiyun
AU - Oliverio, Angela M.
AU - Knight, Rob
N1 - Funding Information:
A.D.W. received support from Duke University Biology Department’s provision of start-up funds for D. Nemergut (deceased) and the Defense Advanced Research Projects Agency (DARPA) grant D16AP0013. This paper is published in the spirit of D. Nemergut’s contagious love of science.
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/6/1
Y1 - 2018/6/1
N2 - How does knowing the evolutionary history of microorganisms affect our analysis of microbiological datasets? Depending on the research question, the common ancestry of microorganisms can be a source of confounding variation, or a scaffolding used for inference. For example, when performing regression on traits, common ancestry is a source of dependence among observations, whereas when searching for clades with correlated abundances, common ancestry is the scaffolding for inference. The common ancestry of microorganisms and their genes are organized in trees-phylogenies-which can and should be incorporated into analyses of microbial datasets. While there has been a recent expansion of phylogenetically informed analytical tools, little guidance exists for which method best answers which biological questions. Here, we review methods for phylogeny-Aware analyses of microbiome datasets, considerations for choosing the appropriate method and challenges inherent in these methods. We introduce a conceptual organization of these tools, breaking them down into phylogenetic comparative methods, ancestral state reconstruction and analysis of phylogenetic variables and distances, and provide examples in Supplementary Online Tutorials. Careful consideration of the research question and ecological and evolutionary assumptions will help researchers choose a phylogeny and appropriate methods to produce accurate, biologically informative and previously unreported insights.
AB - How does knowing the evolutionary history of microorganisms affect our analysis of microbiological datasets? Depending on the research question, the common ancestry of microorganisms can be a source of confounding variation, or a scaffolding used for inference. For example, when performing regression on traits, common ancestry is a source of dependence among observations, whereas when searching for clades with correlated abundances, common ancestry is the scaffolding for inference. The common ancestry of microorganisms and their genes are organized in trees-phylogenies-which can and should be incorporated into analyses of microbial datasets. While there has been a recent expansion of phylogenetically informed analytical tools, little guidance exists for which method best answers which biological questions. Here, we review methods for phylogeny-Aware analyses of microbiome datasets, considerations for choosing the appropriate method and challenges inherent in these methods. We introduce a conceptual organization of these tools, breaking them down into phylogenetic comparative methods, ancestral state reconstruction and analysis of phylogenetic variables and distances, and provide examples in Supplementary Online Tutorials. Careful consideration of the research question and ecological and evolutionary assumptions will help researchers choose a phylogeny and appropriate methods to produce accurate, biologically informative and previously unreported insights.
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U2 - 10.1038/s41564-018-0156-0
DO - 10.1038/s41564-018-0156-0
M3 - Article
C2 - 29795540
AN - SCOPUS:85047810896
SN - 2058-5276
VL - 3
SP - 652
EP - 661
JO - Nature Microbiology
JF - Nature Microbiology
IS - 6
ER -