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

Abstract

Next generation sequencing (NGS) is a trending new standard for genotypic HIV-1 drug resistance (HIVDR) testing. Many NGS HIVDR data analysis pipelines have been independently developed, each with variable outputs and data management protocols. Standardization of such analytical methods and comparison of available pipelines are lacking, yet may impact subsequent HIVDR interpretation and other downstream applications. Here we compared the performance of five NGS HIVDR pipelines using proficiency panel samples from NIAID Virology Quality Assurance (VQA) program. Ten VQA panel specimens were genotyped by each of six international laboratories using their own in-house NGS assays. Raw NGS data were then processed using each of the five different pipelines including HyDRA, MiCall, PASeq, Hivmmer and DEEPGEN. All pipelines detected amino acid variants (AAVs) at full range of frequencies (1~100%) and demonstrated good linearity as compared to the reference frequency values. While the sensitivity in detecting low abundance AAVs, with frequencies between 1~20%, is less a concern for all pipelines, their specificity dramatically decreased at AAV frequencies <2%, suggesting that 2% threshold may be a more reliable reporting threshold for ensured specificity in AAV calling and reporting. More variations were observed among the pipelines when low abundance AAVs are concerned, likely due to differences in their NGS read quality control strategies. Findings from this study highlight the need for standardized strategies for NGS HIVDR data analysis, especially for the detection of minority HiVDR variants.Genotypic HIV drug resistance (HIVDR) testing not only guides effective clinical care of HIV-infected patients but also serves to provide surveillance of transmitted HIVDR in the population. Treatment guidelines in resource-permitted settings advocate the use of HIVDR monitoring both prior to ART initiation and when treatment failure is suspected 1,2 . There is increasing evidence showing that the presence of minority resistance variants open Scientific RepoRtS | (2020) 10:1634 | https://doi.org/10.1038/s41598-020-58544-z www.nature.com/scientificreports www.nature.com/scientificreports/ (MRV) in the HIV quasispecies (i.e., a swarm of highly-related but genotypically different viral variants) may be clinically significant and increase the risk of virological failure, impair immune recovery, lead to accumulation of drug resistance, increase risk of treatment switches and death [3][4][5][6][7][8] . A nationwide study in Mexico focusing on pretreatment drug resistance (PDR) found that lowering the detection threshold for PDR to 5% versus the conventional 20% improved the ability to identify patients with virological failure 6 . In addition, a European wide study found that pre-existing minority drug-resistant HIV-1 variants more than doubled the risk of virological failure to first-line NNRTI-based ART 9 . A more recent African study also reported similar findings, suggesting lowering the threshold below 20% improved the ability to i...

An Overview on SARS-CoV-2 (COVID-19) and Other Human Coronaviruses and Their Detection Capability via Amplification Assay, Chemical Sensing, Biosensing, Immunosensing, and Clinical Assays

“…Chest computed tomography (CT) scan is a kind of radiography and medical imaging using X-ray to figure out any alteration of lung tissue, which sometimes could be helpful to unspecific diagnosis of respiratory viral infections such as coronavirus infection. Regarding the clinical manifestation of COVID-19, CT scan can be used to confirm coronavirus infection as a cause of lung involvement by showing bilateral multilobar ground-glass opacification (GGO) with a peripheral or posterior distribution [ 30 ]. A study in Wuhan, China, on COVID-19 patients, showed that CT scans had a sensitivity of about 98%, which is of particular importance compared to a 71% sensitivity of RT-PCR [ 30 ].…”

“…Regarding the clinical manifestation of COVID-19, CT scan can be used to confirm coronavirus infection as a cause of lung involvement by showing bilateral multilobar ground-glass opacification (GGO) with a peripheral or posterior distribution [ 30 ]. A study in Wuhan, China, on COVID-19 patients, showed that CT scans had a sensitivity of about 98%, which is of particular importance compared to a 71% sensitivity of RT-PCR [ 30 ]. The findings of CT scan are usually unspecific and are the same as other types of pneumonia; however, the combination of this imaging with other diagnostic techniques such as real-time PCR or biosensing as the confirmation could be useful to recognize COVID-19 patients.…”

Applying next-generation sequencing to unravel the mutational landscape in viral quasispecies

“…The other variant callers include ShoRAH (Zagordi et al, 2011) and its extension (McElroy et al, 2013), SNVer (Wei et al, 2011), deepSNV (Gerstung et al, 2012), SAMtools (Li, 2011), GATK (McKenna et al, 2010, Ion-Torrent specific TVC and others. For comparison and evaluation of different methods, please refer to the dedicated reviews or comparative work (Hwang et al, 2015;Lee et al, 2020;Pereira et al, 2020). In short, each method has its own pros and cons.…”

“…Several bioinformatics tools or pipelines for variant calling have been developed specifically for studying viral variants (Huber et al, 2017;McElroy et al, 2013;Verbist et al, 2015;Zagordi et al, 2010) and calculating the complexity of a quasispecies as well as measuring the genetic distance between two similar quasispecies (Marinier et al, 2019). Many more variants callers have been discussed and compared in dedicated reviews or methodological comparison papers (Hwang et al, 2015;Lee et al, 2020;Pereira et al, 2020). Implementing an existing variant-calling tool in NGS data analysis is relatively simple and saves additional costs for sample preparation.…”

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

“…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.…”