In Silico Computational Approach for the Detection of HIV-1 Variants and Drug Resistance Mutations from NGS Data

Abstract

Human immunodeficiency virus type 1 (HIV-1) remains a major global health challenge, largely due to its high mutation and recombination rates, which generate diverse quasispecies within infected individuals. This variability complicates early detection of antiretroviral (ARVà) resistance mutations and limits effective molecular surveillance. Next-generation sequencing (NGS) offers unprecedented resolution for quasispecies analysis, rare variant detection, and early identification of resistance mutations. However, large-scale NGS datasets require robust, reproducible bioinformatics pipelines capable of performing quality control, alignment, variant calling, and consensus sequence generation for downstream phylogenetic and functional analyses. In this study, we developed and validated a fully in silico bioinformatics pipeline for HIV-1 genomic analysis using simulated Illumina sequencing reads derived from the HXB2 reference genome. The pipeline integrates FastQC and MultiQC for quality assessment, Trimmomatic and Cutadapt for read trimming and adapter removal, BWA-MEM and Bowtie2 for reference alignment, SAMtools for sorting and indexing, FreeBayes and GATK HaplotypeCaller for variant calling, bcftools for filtering and consensus sequence generation, IQ-TREE and MEGA for phylogenetic analysis, and Stanford HIVdb for detection and annotation of ARV resistance mutations. Pipeline performance was evaluated using sensitivity, specificity, and accuracy metrics across varying coverage levels (100×, 250×, 500×). Results demonstrated high-quality simulated reads, alignment rates exceeding 97%, and uniform coverage across the HIV-1 genome. Variant calling identified clinically relevant mutations, including K103N, M184V, and Y181C in the reverse transcriptase gene, corresponding to high-level resistance to NNRTIs and NRTIs. The pipeline showed excellent sensitivity and specificity in detecting known variants, with reproducible results across coverage conditions. Comparative analysis with published studies confirmed concordance with established NGS-based HIV-1 resistance detection. Overall, this in silico pipeline provides a reliable, reproducible, and adaptable framework for HIV-1 genomic analysis and ARV resistance mutation detection. It enables rigorous method evaluation without clinical samples, supports molecular surveillance, and can inform therapeutic decision-making, particularly in resource-limited settings. Future work will extend the pipeline to clinical datasets, low-frequency variant detection, and further automation.