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Updated: Jun 22, 2026

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

The HIV positive selection mutation database.

Calvin Pan¹, Joseph Kim, Lamei Chen

¹Center for Computational Biology, University of California Los Angeles, CA, USA.

Nucleic Acids Research

|November 17, 2006

Summary

This summary is machine-generated.

The HIV Positive Selection Mutation Database offers detailed selection pressure maps for HIV protease and reverse transcriptase, aiding antiretroviral therapy research. It utilizes a large dataset from clinical AIDS samples for high-resolution analysis.

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Area of Science:

Bioinformatics
Virology
Genomics

Background:

Antiretroviral therapy targets HIV protease and reverse transcriptase.
Understanding mutations in these enzymes is crucial for effective treatment.
Previous databases lacked high-resolution selection pressure mapping.

Purpose of the Study:

To create a comprehensive database of HIV selection pressures.
To enable high-resolution mapping of mutations in HIV protease and reverse transcriptase.
To facilitate the analysis of how mutations at one site affect selection pressure at other sites.

Main Methods:

Development of the HIV Positive Selection Mutation Database.
Integration of a large dataset from approximately 50,000 clinical AIDS samples.
Inclusion of data from public databases like the Stanford HIV database.
Analysis of selection pressure on individual sites and inter-site mutation effects.

Main Results:

Provides detailed selection pressure maps for HIV protease and reverse transcriptase.
Enables high-resolution mapping due to a large HIV sequence dataset.
Offers insights into the interplay of mutations at different genetic sites.
Facilitates cross-validation with independent datasets.

Conclusions:

The database is a valuable resource for understanding HIV evolution under drug pressure.
High-resolution selection pressure mapping can inform the development of new antiretroviral therapies.
Analysis of mutation interactions aids in predicting treatment resistance patterns.