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MBE Advance Access originally published online on July 3, 2006
Molecular Biology and Evolution 2006 23(10):1891-1901; doi:10.1093/molbev/msl051
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© 2006 The Authors 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.

Research Article

Automated Phylogenetic Detection of Recombination Using a Genetic Algorithm

Sergei L. Kosakovsky Pond*, David Posada{dagger}, Michael B. Gravenor{ddagger}, Christopher H. Woelk* and Simon D. W. Frost*

* Department of Pathology, University of California San Diego; {dagger} University of Vigo, Vigo, Spain; and {ddagger} School of Medicine, University of Swansea, Swansea, Wales, United Kingdom

E-mail: spond{at}ucsd.edu.

The evolution of homologous sequences affected by recombination or gene conversion cannot be adequately explained by a single phylogenetic tree. Many tree-based methods for sequence analysis, for example, those used for detecting sites evolving nonneutrally, have been shown to fail if such phylogenetic incongruity is ignored. However, it may be possible to propose several phylogenies that can correctly model the evolution of nonrecombinant fragments. We propose a model-based framework that uses a genetic algorithm to search a multiple-sequence alignment for putative recombination break points, quantifies the level of support for their locations, and identifies sequences or clades involved in putative recombination events. The software implementation can be run quickly and efficiently in a distributed computing environment, and various components of the methods can be chosen for computational expediency or statistical rigor. We evaluate the performance of the new method on simulated alignments and on an array of published benchmark data sets. Finally, we demonstrate that prescreening alignments with our method allows one to analyze recombinant sequences for positive selection.

Key Words: recombination • phylogenetic incongruence • model selection • genetic algorithms • multimodel inference


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