Journal Article

Phylogenetic inference under recombination using Bayesian stochastic topology selection

Alex Webb, John M. Hancock and Chris C. Holmes

in Bioinformatics

Volume 25, issue 2, pages 197-203
Published in print January 2009 | ISSN: 1367-4803
Published online November 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn607

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Motivation: Conventional phylogenetic analysis for characterizing the relatedness between taxa typically assumes that a single relationship exists between species at every site along the genome. This assumption fails to take into account recombination which is a fundamental process for generating diversity and can lead to spurious results. Recombination induces a localized phylogenetic structure which may vary along the genome. Here, we generalize a hidden Markov model (HMM) to infer changes in phylogeny along multiple sequence alignments while accounting for rate heterogeneity; the hidden states refer to the unobserved phylogenic topology underlying the relatedness at a genomic location. The dimensionality of the number of hidden states (topologies) and their structure are random (not known a priori) and are sampled using Markov chain Monte Carlo algorithms. The HMM structure allows us to analytically integrate out over all possible changepoints in topologies as well as all the unknown branch lengths.

Results: We demonstrate our approach on simulated data and also to the genome of a suspected HIV recombinant strain as well as to an investigation of recombination in the sequences of 15 laboratory mouse strains sequenced by Perlegen Sciences. Our findings indicate that our method allows us to distinguish between rate heterogeneity and variation in phylogeny caused by recombination without being restricted to 4-taxa data.

Availability: The method has been implemented in JAVA and is available, along with data studied here, from http://www.stats.ox.ac.uk/~webb.

Contact: cholmes@stats.ox.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5292 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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