HIV-Quasipore: A Suite of HIV-1-Specific Nanopore Basecallers Designed to Enhance Viral Quasispecies Detection - Link, Robert W.; De Souza, Diehl R.; Spector, Cassandra; Mele, Anthony R.; Chung, Cheng-Han; Nonnemacher, Michael R.; Wigdahl, Brian; Dampier, Will (Frontiers in Virology, 2022)
Accounting for genetic variation is an obligate consideration during human immunodeficiency virus type 1 (HIV-1) investigation. Nanopore sequencing preserves proviral integrity by passing long genomic fragments through ionic channels, allowing reads that span the entire genome of different viral quasispecies (vQS). However, this sequencing method has suffered from high error rates, limiting its utility. This was the inspiration behind HIV-Quasipore: an HIV-1-specific Nanopore basecaller suite designed to overcome these error rates through training with gold-standard data. It is comprised of three deep learning-based R9.4.1 basecallers: fast, high accuracy (HAC), super accuracy (SUP), and two R10.3 deep learning-based basecallers: HAC and SUP. This was accomplished by sequencing the HIV-1 J-Lat 10.6 cell line using Nanopore and high-quality Sanger techniques. Training significantly reduced basecaller error rates across all models (Student’s one-sided t-test; p = 0.0) where median error rates were 0.0189, 0.0018, 0.0008, for R9.4.1 HIV-Quasipore-fast, HAC, SUP, and 0.0007, 0.0011 for R10.3 HIV-Quasipore-HAC, and SUP, respectively. This improved quality reduces the resolution needed to accurately detect a vQS from 22.4% to 2.6% of total positional coverage for R9.4.1 HIV-Quasipore-fast, 6.9% to 0.5% for R9.4.1 HIV-Quasipore-HAC, 4.5% to 0.3% for R9.4.1 HIV-Quasipore-SUP, 8.0% to 0.3% for R10.3 HIV-Quasipore-HAC, 5.4% to 0.3% for R10.3 HIV-Quasipore-SUP. This was consistently observed across the entire J-Lat 10.6 genome and maintained across longer reads. Reads greater than 8000 nucleotides display a median nucleotide identity of 0.9819, 0.9982, 0.9991, for R9.4.1 HIV-Quasipore-fast, HAC, SUP, and 0.9993, 0.9988 for R10.3 HIV-Quasipore-HAC, and SUP, respectively. To evaluate the robustness of this tool against unseen data, HIV-Quasipore and their corresponding pretrained basecallers were used to sequence the J-Lat 9.2 cell line and a clinical isolate acquired from the Drexel Medicine CARES cohort. When sample reads were compared against their corresponding consensus sequence, all HIV-Quasipore basecallers displayed higher median alignment accuracies than their pretrained counterparts for both the J-Lat 9.2 cell line and clinical isolate. Using Nanopore sequencing can allow investigators to explore topics, such as vQS profile detection, HIV-1 integration site analysis, whole genome amplification, gene coevolution, and CRISPR-induced indel detection, among others. HIV-Quasipore basecallers can be acquired here: https://github.com/DamLabResources/HIV-Quasipore-basecallers.