Efficient inference of bacterial strain trees from genome-scale multilocus data
- Rice Univ., Houston, TX (United States). Dept. of Computer Science
- Hayama Univ., Kanagawa (Japan). School of Advanced Sciences. Graduate Univ. for Advanced Studies. Dept. of Evolutionary Studies of Biosystems
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/
- Research Organization:
- Rice Univ., Houston, TX (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- Grant/Contract Number:
- FG02-06ER25734
- OSTI ID:
- 1625256
- Journal Information:
- Bioinformatics, Vol. 24, Issue 13; ISSN 1367-4803
- Publisher:
- International Society for Computational Biology - Oxford University PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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