Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance
Identifieur interne : 001680 ( Main/Exploration ); précédent : 001679; suivant : 001681Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance
Auteurs : K. Deforche ; T. Silander [Finlande] ; R. Camacho [Portugal] ; Z Grossman [Israël] ; M. A. Soares ; K. Van Laethem ; R. Kantor [États-Unis] ; Y. Moreau [Belgique] ; A.-M. VandammeSource :
- Bioinformatics [ 1367-4803 ] ; 2006-12-15.
Abstract
Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway. Contact:koen.deforche@uz.kuleuven.ac.be Supplementary information: Supplementary data are available at Bioinformatics online.
Url:
DOI: 10.1093/bioinformatics/btl508
Affiliations:
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Vandamme</name></author></analytic><monogr></monogr><series><title level="j">Bioinformatics</title><idno type="ISSN">1367-4803</idno><idno type="eISSN">1460-2059</idno><imprint><publisher>Oxford University Press</publisher><date type="published" when="2006-12-15">2006-12-15</date><biblScope unit="volume">22</biblScope><biblScope unit="issue">24</biblScope><biblScope unit="page" from="2975">2975</biblScope><biblScope unit="page" to="2979">2979</biblScope></imprint><idno type="ISSN">1367-4803</idno></series><idno type="istex">8A99BBB12A3CF2D7F65044DB63D145F9EC939062</idno><idno type="DOI">10.1093/bioinformatics/btl508</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1367-4803</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway. 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