title: A Bayesian approach to inferring the phylogenetic structure of communities from metagenomic data.
abstract: 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.
authors.affiliation: Department of Mathematics, Bowdoin College, Brunswick, Maine 04011 jobrien@bowdoin.edu daniel_falush@eva.mpg.de.
authors.forename: John D
authors.initials: JD
authors.lastname: O'Brien
authors.affiliation: School of Public Health, Imperial College London, London W2 1PG, United Kingdom.
authors.forename: Xavier
authors.initials: X
authors.lastname: Didelot
authors.affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom.
authors.forename: Zamin
authors.initials: Z
authors.lastname: Iqbal
authors.affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom.
authors.forename: Lucas
authors.initials: L
authors.lastname: Amenga-Etego
authors.affiliation: Department of Statistics, University of Oxford, Oxford OX1 3TG, United Kingdom.
authors.forename: Bartu
authors.initials: B
authors.lastname: Ahiska
authors.affiliation: Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany jobrien@bowdoin.edu daniel_falush@eva.mpg.de.
authors.forename: Daniel
authors.initials: D
authors.lastname: Falush
mesh_terms: D000465:Algorithms; D000864:Antarctic Regions; D001499:Bayes Theorem; D019414:Chlorobi / Q000145:classification; D003198:Computer Simulation; D030541:Databases, Genetic; D005869:Ghana; D006801:Humans; D060106:Lakes / Q000382:microbiology; D016778:Malaria, Falciparum / Q000469:parasitology; D056186:Metagenomics; D008954:Models, Biological; D009345:Neisseria meningitidis / Q000502:physiology; D010802:Phylogeny; D010963:Plasmodium falciparum / Q000502:physiology; D020641:Polymorphism, Single Nucleotide / Q000235:genetics
keywords: Bayesian phylogenetics; metagenomics; microevolution