Detecting recombination in evolving nucleotide sequences.
BACKGROUND
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.


CONCLUSIONS
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.
Chan
Cheong Xin
CX
ARC Centre in Bioinformatics and Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia. c.chan@imb.uq.edu.au
Beiko
Robert G
RG
Ragan
Mark A
MA
D000465:Algorithms; D001483:Base Sequence; D001499:Bayes Theorem; D003198:Computer Simulation; D019143:Evolution, Molecular; D016014:Linear Models; D008957:Models, Genetic; D010802:Phylogeny; D011995:Recombination, Genetic; D012984:Software