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Bioinformatics 2008 24(13):i123-i131; doi:10.1093/bioinformatics/btn149
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Efficient inference of bacterial strain trees from genome-scale multilocus data

C. Than 1, R. Sugino 2, H. Innan 2 and L. Nakhleh 1,*

1Department of Computer Science, Rice University, Houston, TX 77005, USA and 2Department of Evolutionary Studies of Biosystems, Graduate University for Advanced Studies, School of Advanced Sciences, Hayama, Kanagawa 240-0193, Japan

*To whom correspondence should be addressed.


  Abstract

Motivation: In bacterial evolution, inferring a strain tree, which is the evolutionary history of different strains of the same bacterium, plays a major role in analyzing and understanding the evolution of strongly isolated populations, population divergence and various evolutionary events, such as horizontal gene transfer and homologous recombination. Inferring a strain tree from multilocus data of these strains is exceptionally hard since, at this scale of evolution, processes such as homologous recombination result in a very high degree of gene tree incongruence.

Results: In this article we present a novel computational method for inferring the strain tree despite massive gene tree incongruence caused by homologous recombination. Our method operates in three phases, where in phase I a set of candidate strain-tree topologies is computed using the maximal cliques concept, in phase II divergence times for each of the topologies are estimated using mixed integer linear programming (MILP) and in phase III the optimal tree (or trees) is selected based on an optimality criterion. We have analyzed 1898 genes from nine strains of the Staphylococcus aureus bacteria, and identified a fully resolved (binary) strain tree with estimated divergence times, despite the high degrees of sequence identity at the nucleotide level and gene tree incongruence. Our method's efficiency makes it particularly suitable for analysis of genome-scale datasets, including those of strongly isolated populations which are usually very challenging to analyze.

Availability: We have implemented the algorithms in the PhyloNet software package, which is available publicly at http://bioinfo.cs.rice.edu/phylonet/

Contact: nakhleh{at}cs.rice.edu


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