A number of statistical techniques have been developed to test for the occurrence of recombination within a given gene region and to determine the bounds of the recombinational event. Unfortunately, we know very little about the relative strengths and weaknesses of these techniques. We propose to evaluate the performance of different methods to detect recombination from a set of aligned DNA sequences. The statistical evaluation of these methods will provide valuable information to select a method for detecting recombination. Moreover, the identification of the conditions under which different methods are more likely to be misleading will provide more robust conclusions about the occurrence of recombination. By understanding which factors are most relevant in the detection of recombination, we will be able to develop better methods and to improve existing techniques. Finally, the best performing techniques will be used to scan the HIV database for the presence of recombination and for the reevaluation of circulating recombinant forms (CRF). Understanding the generation of genetic diversity of HIV-1 is key to fighting its ability to evolve solutions to drug therapies and vaccine attempts. Recombination and mutation are the creative forces for genetic diversity. Studies in HIV-1 have shown recombination to have a significant impact on our understanding of the history of gene genealogies and arguments based on these phylogenies, including estimates of genetic diversity. Our study will provide researchers with an assessment of the available statistical tools for detecting recombination and guidelines as to when each tool works best. It will also provide the first comprehensive assessment of the impact of recombination in shaping the current genetic diversity of HIV-1 using statistically validated techniques.
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