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An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

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Development of a Subsequence Correlation Coefficient Feature Vector Method for High-Resolution HIV-1 Subtype

Shuyan Han¹, Lily He¹, Yihang Tang²

¹School of Science, Beijing University of Civil Engineering and Architecture, Beijing, China.

China CDC Weekly

|May 22, 2026

Summary

This summary is machine-generated.

A new alignment-free method accurately classifies HIV-1 subtypes using multigene models. This tool enhances molecular surveillance for non-B strains in China, aiding targeted public health strategies.

Keywords:

China HIV-1 SCCFV Subtype

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Area of Science:

Virology
Computational Biology
Genetics

Background:

Current HIV-1 subtype classification methods are often time-consuming.
Existing non-alignment methods typically focus on single genes.
China requires a specific model for predicting prevalent non-B subtype strains.

Purpose of the Study:

To develop a rapid, alignment-free method for HIV-1 multigene model building.
To create a tool for accurate classification of HIV-1 subtypes, particularly non-B strains in China.

Main Methods:

Developed a novel alignment-free computational method named SCCFV-RDA.
Utilized multigene models for enhanced classification accuracy.
Applied the method to international and Chinese HIV-1 datasets.

Main Results:

Achieved over 99.85% accuracy on an international dataset.
Attained 99.7% accuracy on Chinese pol gene data.
Demonstrated superior recall for key circulating recombinant form subtypes.

Conclusions:

The SCCFV-RDA tool offers accurate and efficient HIV-1 subtype classification.
This method supports precise molecular surveillance of HIV-1 in China.
Facilitates the development of targeted prevention and control strategies for HIV-1.