A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data

A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data

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 th...

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Main Authors: O’Brien, John D., Didelot, Xavier, Iqbal, Zamin, Amenga-Etego, Lucas, Ahiska, Bartu, Falush, Daniel
Format: Online
Language:English
Published: Genetics Society of America 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4096371/
id pubmed-4096371
recordtype oai_dc
spelling pubmed-40963712014-07-16 A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data O’Brien, John D. Didelot, Xavier Iqbal, Zamin Amenga-Etego, Lucas Ahiska, Bartu Falush, Daniel Investigations 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. Genetics Society of America 2014-07 2014-05-01 /pmc/articles/PMC4096371/ /pubmed/24793089 http://dx.doi.org/10.1534/genetics.114.161299 Text en Copyright © 2014 by the Genetics Society of America Available freely online through the author-supported open access option.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author O’Brien, John D.
Didelot, Xavier
Iqbal, Zamin
Amenga-Etego, Lucas
Ahiska, Bartu
Falush, Daniel
spellingShingle O’Brien, John D.
Didelot, Xavier
Iqbal, Zamin
Amenga-Etego, Lucas
Ahiska, Bartu
Falush, Daniel
A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data
author_facet O’Brien, John D.
Didelot, Xavier
Iqbal, Zamin
Amenga-Etego, Lucas
Ahiska, Bartu
Falush, Daniel
author_sort O’Brien, John D.
title A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data
title_short A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data
title_full A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data
title_fullStr A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data
title_full_unstemmed A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data
title_sort bayesian approach to inferring the phylogenetic structure of communities from metagenomic data
description 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.
publisher Genetics Society of America
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4096371/
_version_ 1613113261314539520

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