The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

MOTIVATION: Genetic analysis of HIV-1 is important not only for vaccine development, but also to guide treatment strategies, track the emergence of new viral variants and ensure that diagnostic assays are contemporary and fully optimized. However, most genotyping methods are laborious and complex, and involve the use of multiple software applications. Here, we describe the development of an automated genotyping system that can be easily applied to HIV-1 and other rapidly evolving viral pathogens. RESULTS: The new REGA subtyping tool, developed using Java programming and PERL scripts, combines phylogenetic analyses with boot-scanning methods for the genetic subtyping of full-length and subgenomic fragments of HIV-1. When used to investigate the subtype of previously published reference datasets that were analysed using manual phylogenetic methods, the automated method correctly identified 97.5-100% of non-recombinant and circulating recombinant forms of HIV-1, including 108 full-length, 108 gag and 221 env sequences downloaded from the Los Alamos database.