ETI

ETI estimates the time since HIV-1 infection using next-generation sequencing (NGS) data by quantifying viral diversity as a temporal biomarker.

Key Features:

• NGS-Based Viral Diversity Measurement: Uses viral diversity derived from next-generation sequencing data to estimate time since infection (TI).
• Continuous Diversity Metrics: Calculates diversity using continuous measures including average pairwise distance and site entropy.
• pol Gene Third Codon Position Analysis: Analyzes third codon positions in the HIV-1 pol gene, where viral diversity increases approximately linearly over time with limited inter-patient variation.
• Sequencing Depth Sensitivity: Improves estimation precision with increasing sequencing depth from deep NGS datasets.
• Regression-Based Time Estimation: Uses regression coefficients derived from longitudinal HIV-1 datasets to estimate infection timing.

Scientific Applications:

• HIV Epidemiology Studies: Estimates time since HIV-1 infection to support analysis of transmission dynamics and epidemic patterns.
• Infection Timeline Reconstruction: Reconstructs infection timing in longitudinal studies of HIV-1–infected individuals.
• Biomarker-Based Infection Dating: Applies viral diversity metrics as biomarkers for estimating infection duration.

Methodology:

ETI calculates viral diversity from next-generation sequencing data using average pairwise distance or site entropy, focuses on third codon positions in the HIV-1 pol gene, and applies regression coefficients derived from longitudinal datasets to estimate time since infection.