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dc.contributor.authorChan, Cheongen_US
dc.contributor.authorBeiko, Roberten_US
dc.contributor.authorRagan, Marken_US
dc.date.accessioned2013-08-08T18:22:50Z
dc.date.available2013-08-08T18:22:50Z
dc.date.issued2006en_US
dc.identifier.citationChan, Cheong, Robert Beiko, and Mark Ragan. 2006. "Detecting recombination in evolving nucleotide sequences." BMC Bioinformatics 7(1): 412.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://hdl.handle.net/10222/34701
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2105-7-412
dc.description.abstractBACKGROUND:Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. These recombination events can be obscured by subsequent residue substitutions, which consequently complicate their detection. While there are many algorithms for the identification of recombination events, little is known about the effects of subsequent substitutions on the accuracy of available recombination-detection approaches.RESULTS:We assessed the effect of subsequent substitutions on the detection of simulated recombination events within sets of four nucleotide sequences under a homogeneous evolutionary model. The amount of subsequent substitutions per site, prior evolutionary history of the sequences, and reciprocality or non-reciprocality of the recombination event all affected the accuracy of the recombination-detecting programs examined. Bayesian phylogenetic-based approaches showed high accuracy in detecting evidence of recombination event and in identifying recombination breakpoints. These approaches were less sensitive to parameter settings than other methods we tested, making them easier to apply to various data sets in a consistent manner.CONCLUSION:Post-recombination substitutions tend to diminish the predictive accuracy of recombination-detecting programs. The best method for detecting recombined regions is not necessarily the most accurate in identifying recombination breakpoints. For difficult detection problems involving highly divergent sequences or large data sets, different types of approach can be run in succession to increase efficiency, and can potentially yield better predictive accuracy than any single method used in isolation.en_US
dc.relation.ispartofBMC Bioinformaticsen_US
dc.titleDetecting recombination in evolving nucleotide sequencesen_US
dc.typearticleen_US
dc.identifier.volume7en_US
dc.identifier.issue1en_US
dc.identifier.startpage412en_US
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