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Metagenomics provides a powerful new tool set for investigating evolutionary interactions with the environment. However, an absence of model-based statistical methods means that researchers are often not able to make full use of this complex information. We present a Bayesian method for inferring the phylogenetic relationship among related organisms found within metagenomic samples. Our approach exploits variation in the frequency of taxa among samples to simultaneously infer each lineage haplotype, the phylogenetic tree connecting them, and their frequency within each sample. Applications of the algorithm to simulated data show that our method can recover a substantial fraction of the phylogenetic structure even in the presence of high rates of migration among sample sites. We provide examples of the method applied to data from green sulfur bacteria recovered from an Antarctic lake, plastids from mixed Plasmodium falciparum infections, and virulent Neisseria meningitidis samples.

Original publication

DOI

10.1534/genetics.114.161299

Type

Journal article

Journal

Genetics

Publication Date

07/2014

Volume

197

Pages

925 - 937

Keywords

Bayesian phylogenetics, metagenomics, microevolution, Algorithms, Antarctic Regions, Bayes Theorem, Chlorobi, Computer Simulation, Databases, Genetic, Ghana, Humans, Lakes, Malaria, Falciparum, Metagenomics, Models, Biological, Neisseria meningitidis, Phylogeny, Plasmodium falciparum, Polymorphism, Single Nucleotide