The MODELTEST package, including the source code and some documentation is available at http://bioag.byu. edu/zoology/crandall_lab/modeltest.html.

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Recombination is a key evolutionary process that shapes the architecture of genomes and the genetic structure of populations. Although many statistical methods are available for the detection of recombination from DNA sequences, their absolute and relative performance is still unknown. Here we evaluated the performance of 14 different recombination detection algorithms. We used the coalescent with recombination to simulate DNA sequences with different levels of recombination, genetic diversity, and rate variation among sites. Recombination detection methods were applied to these data sets, and whether they detected or not recombination was recorded. Different recombination methods showed distinct performance depending on the amount of recombination, genetic diversity, and rate variation among sites. The model of nucleotide substitution under which the data were generated did not seem to have a significant effect. Most methods increase power with more sequence divergence. In general, recombination detection methods seem to capture the presence of recombination, but they are not very powerful. Methods that use substitution patterns or incompatibility among sites were more powerful than methods based on phylogenetic incongruence. Most methods do not seem to infer more false positives than expected by chance. Especially depending on the amount of diversity in the data, different methods could be used to attain maximum power while minimizing false positives. Results shown here will provide some guidance in the selection of the most appropriate method͞s for the analysis of the particular data at hand. R ecombination, defined here as the exchange of genetic information between two nucleotide sequences, is an important process that influences biological evolution at many different levels. Recombination explains a considerable amount of genetic diversity in natural populations and, in general, genes located in regions of the genome with low levels of recombination have low levels of polymorphism. Recombination reshuffles existing variation and even creates new variants at the amino acid level. Indeed, recombination shapes the genetic structure of natural populations (1, 2) and the action of natural selection (3). Characterization of the role of recombination across genomes is of major interest. The study of recombination events will allow us to better understand the dynamics of genomes (4, 5). Recombination breaks down linkage disequilibrium and, consequently, the characterization of recombination is essential for gene mapping, quantitative trait loci, and association studies (6). In addition, recombination has a significant impact on the evolution of several human pathogens (7-9) and consequently on their clinical treatment and prevention. Moreover, many applications in biology today are based on the estimation of phylogenetic trees. One main assumption of most phylogenetic methods is that there is only one phylogeny underlying the evolution of the sequences under study. Recombination violates this assumption by generat...