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Modeling HIV-1 drug resistance as episodic directional selection

dc.contributor.authorMurrell, Ben
dc.contributor.authorDe Oliveira, Tulio
dc.contributor.authorSeebregts, Chris
dc.contributor.authorPond, Sergei L. Kosakovsky
dc.contributor.authorScheffler, Konrad
dc.date.accessioned2013-02-25T14:07:10Z
dc.date.available2013-02-25T14:07:10Z
dc.date.issued2011-05
dc.identifier.citationMurrell, B. et al. 2012. Modeling HIV-1 drug resistance as episodic directional selection. PLoS Computational Biology, 8(5):1-10. doi:10.1371/journal.pcbi.1002507.en_ZA
dc.identifier.issn1553-7358 (online)
dc.identifier.issn1553-734X (print)
dc.identifier.otherdoi:10.1371/journal.pcbi.1002507
dc.identifier.urihttp://hdl.handle.net/10019.1/79607
dc.descriptionThe original publication is available at www.ploscompbiol.orgen_ZA
dc.description.abstractThe 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. Whilemethods 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 (MEDSand) 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.en_ZA
dc.description.sponsorshipThis research was supported by Europeaid Grant number SANTE/2007/147-790 from the European Commission; BM is supported by the same Europeaid Grant. TdO’s work on this paper was funded by the same Europeaid grant, by the Africa Centre for Health and Population Studies Wellcome Trust Core Grant 082384/Z/07/Z and the grant entitled Swiss-Prot/South Africa: Protein Bioinformatics Resource Development for Important Health-related Pathogens’’ under the Switzerland-South Africa Collaborative Research Program. Funding for the UCSD computing cluster has been provided by the Joint DMS/NIGMS Mathematical Biology Initiative through Grant NSF-0714991 and the National Institutes of Health grants AI47745 and AI74621. HyPhy custom script development was supported by the National Institute Of General Medical Sciences (grant GM093939). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscripten_ZA
dc.format.extent10 p. : ill.
dc.publisherPLOS Computational Biologyen_ZA
dc.subjectHIV infectionsen_ZA
dc.subjectDrug resistanceen_ZA
dc.titleModeling HIV-1 drug resistance as episodic directional selectionen_ZA
dc.typeArticleen_ZA
dc.description.versionPublishers versionen_ZA
dc.rights.holderThe author holds the copyrighten_ZA


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