Title |
Modeling HIV-1 Drug Resistance as Episodic Directional Selection
|
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Published in |
PLoS Computational Biology, May 2012
|
DOI | 10.1371/journal.pcbi.1002507 |
Pubmed ID | |
Authors |
Ben Murrell, Tulio de Oliveira, Chris Seebregts, Sergei L. Kosakovsky Pond, Konrad Scheffler |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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South Africa | 1 | 20% |
Canada | 1 | 20% |
United States | 1 | 20% |
United Kingdom | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 40% |
Science communicators (journalists, bloggers, editors) | 1 | 20% |
Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Colombia | 1 | 2% |
Netherlands | 1 | 2% |
France | 1 | 2% |
United Kingdom | 1 | 2% |
Canada | 1 | 2% |
Taiwan | 1 | 2% |
United States | 1 | 2% |
Unknown | 50 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 19% |
Researcher | 11 | 19% |
Student > Bachelor | 8 | 14% |
Student > Master | 6 | 11% |
Professor | 5 | 9% |
Other | 10 | 18% |
Unknown | 6 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 24 | 42% |
Computer Science | 6 | 11% |
Biochemistry, Genetics and Molecular Biology | 5 | 9% |
Medicine and Dentistry | 4 | 7% |
Immunology and Microbiology | 4 | 7% |
Other | 8 | 14% |
Unknown | 6 | 11% |