G-HIV: An Integrated Long-Read Sequencing and Automated Bioinformatics Platform for Rapid and Precise HIV-1 Surveillance

The accurate characterization of human immunodeficiency virus (HIV) genetic diversity and drug resistance is critical for effective surveillance and treatment, yet current sequencing technologies face limitations in sensitivity and scalability for community-level implementation. We present G-HIV, an integrated platform combining long-read sequencing (G-seq500) with an automated bioinformatics pipeline. G-HIV processes raw FastQ data to generate automated reports on point mutations, drug resistance predictions, viral quasispecies diversity, and haplotype networks via a two-step analytical approach. Applied to 44 HIV-1 plasma samples (42 used in the final comparison after excluding 2 samples with low-quality Sanger chromatograms), G-HIV detected 3–48 candidate minority variants per sample that were not observed by Sanger sequencing, identifying drug-resistant quasispecies in two samples with undetectable Sanger signals, and revealed mixed infection cases (e.g., inter-subtype CRF07_BC/CRF08_BC) through phylogenetic analysis. G-HIV addresses an integration of long-read sequencing with a fully automated, one-stop bioinformatics pipeline designed for frontline laboratories without specialized bioinformatics expertise—providing a scalable solution for community-based resistance surveillance and personalized therapy optimization in resource-limited settings. This research addresses an integrated long-read sequencing and automated bioinformatics platform for rapid and precise HIV-1 surveillance. G-HIV surpasses conventional approaches like Sanger sequencing in resolution, efficiency, and accessibility for community-level surveillance. By integrating long-read sequencing, streamlining workflows and eliminating the need for specialized bioinformatics expertise, G-HIV is positioned to become a new solution, providing more effective one-stop services for HIV-1 prevention and control.