A MiSeq-HyDRA platform for enhanced HIV drug resistance genotyping and surveillance

Abstract

Conventional HIV drug resistance (HIVDR) genotyping utilizes Sanger sequencing (SS) methods, which are limited by low data throughput and the inability of detecting low abundant drug resistant variants (LADRVs). Here we present a next generation sequencing (NGS)-based HIVDR typing platform that leverages the advantages of Illumina MiSeq and HyDRA Web. The platform consists of a fully validated sample processing protocol and HyDRA web, an open web portal that allows automated customizable NGS-based HIVDR data processing. This platform was characterized and validated using a panel of HIV-spiked plasma representing all major HIV-1 subtypes, pedigreed plasmids, HIVDR proficiency specimens and clinical specimens. All examined major HIV-1 subtypes were consistently amplified at viral loads of ≥1,000 copies/ml. The gross error rate of this platform was determined at 0.21%, and minor variations were reliably detected down to 0.50% in plasmid mixtures. All HIVDR mutations identifiable by SS were detected by the MiSeq-HyDRA protocol, while LADRVs at frequencies of 1~15% were detected by MiSeq-HyDRA only. As compared to SS approaches, the MiSeq-HyDRA platform has several notable advantages including reduced cost and labour, and increased sensitivity for LADRVs, making it suitable for routine HIVDR monitoring for both patient care and surveillance purposes.

Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing

“…This additional information strengthens our ability to assess the clinical impact of a given DRM and to determine and track its overall frequency within a population, which may significantly impact drug regimens and public health approaches to control and reduce HIV transmission 10,14,[49][50][51][52] . Notably, while many NGS HIVDR data analysis pipelines exist 25,[31][32][33][34][35][36][37][38][39] , their design and implementation was conducted independently by different research groups with little coordination among the developers. Given the complexity of the analysis and the varied approaches adopted by the different development teams, this historical lack of coordination results in uncertainties in the reliability of the data.…”

“…The standardization of NGS-based HIVDR assays is more complex and it includes three main steps: (1) wet-lab steps to generate PCR amplicons that cover the pol region and prepare libraries; (2) NGS platforms; and (3) bioinformatics pipelines which convert NGS data into user-interpretable HIVDR results 13,15,30 . Several bioinformatical pipelines have been independently developed to address the needs for automated NGS-based HIVDR genotyping 25,[31][32][33][34][35][36][37][38][39] . We recently published guidelines on the standards for bioinformatics analysis and reporting conventions for HIVDR research and clinical purposes in the "Winnipeg Consensus".…”

Analysis of unusual and signature APOBEC-mutations in HIV-1polnext-generation sequences

“…We extended the HYDRA pipeline [14] to generate a codon frequency table from each FASTQ file. Briefly, we filtered reads with fewer than 100 nucleotides or a mean quality (phred or score <30 (predicted error rate 1 in 1000).…”

V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput sequencing data

“…Many workflows have been designed for virus discovery and metagenomics applications (Ho and Tzanetakis, 2014;Naccache et al, 2014;Li et al, 2016;Zhao et al, 2017;Zheng et al, 2017;Maarala et al, 2017). Other pipelines focus on either (i) the construction of the consensus sequence via reference-guided assembly, such as VirAmp (Wan et al, 2015) and shiver (Wymant et al, 2018), (ii) the identification of SNVs, such as hivmmer (Howison et al, 2018), HyDRA (Taylor et al, 2019) and MinVar (Huber et al, 2017), or (iii) the reconstruction of viral haplotypes, such as VGA (Mangul et al, 2014) and ViQuaS (Jayasundara et al, 2015). Often these tools either target specific viruses (Huber et al, 2017;Wymant et al, 2018;Howison et al, 2018;Taylor et al, 2019) or correspond to proof-of-concept implementations with limited support for broader applications.…”

“…Other pipelines focus on either (i) the construction of the consensus sequence via reference-guided assembly, such as VirAmp (Wan et al, 2015) and shiver (Wymant et al, 2018), (ii) the identification of SNVs, such as hivmmer (Howison et al, 2018), HyDRA (Taylor et al, 2019) and MinVar (Huber et al, 2017), or (iii) the reconstruction of viral haplotypes, such as VGA (Mangul et al, 2014) and ViQuaS (Jayasundara et al, 2015). Often these tools either target specific viruses (Huber et al, 2017;Wymant et al, 2018;Howison et al, 2018;Taylor et al, 2019) or correspond to proof-of-concept implementations with limited support for broader applications. For HIV drug resistance testing, Lee et al (2020) have recently evaluated various bioinformatics pipelines for this specific application.…”