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Since recombination leads to the generation of mosaic genomes that violate the assumption of traditional phylogenetic methods that sequence evolution can be accurately described by a single tree, results and conclusions based on phylogenetic analysis of data sets including recombinant sequences can be severely misleading. Many methods are able to adequately detect recombination between diverse sequences, for example between different HIV-1 subtypes. More problematic is the identification of recombinants among closely related sequences such as a viral population within a host. We describe a simple algorithmic procedure that enables detection of intra-host recombinants based on split-decomposition networks and a robust statistical test for recombination. By applying this algorithm to several published HIV-1 data sets we conclude that intra-host recombination was significantly underestimated in previous studies and that up to one-third of the env sequences longitudinally sampled from a given subject can be of recombinant origin. The results show that our procedure can be a valuable exploratory tool for detection of recombinant sequences before phylogenetic analysis, and also suggest that HIV-1 recombination in vivo is far more frequent and significant than previously thought.

Original publication

DOI

10.1016/j.ympev.2008.08.017

Type

Journal article

Journal

Mol Phylogenet Evol

Publication Date

11/2008

Volume

49

Pages

618 - 628

Keywords

Algorithms, DNA, Viral, Evolution, Molecular, Genetics, Population, HIV Infections, HIV-1, Humans, Models, Genetic, Phylogeny, Recombination, Genetic, Sequence Alignment, Sequence Analysis, DNA, Statistics, Nonparametric