Promises and pitfalls of Illumina sequencing for HIV resistance genotyping

HIV-1 Low-Frequency Variants Identified in Antiretroviral-Naïve Subjects with Virologic Failure after 12 Months of Follow-Up in Panama

Low-frequency mutations associated with drug resistance have been related to virologic failure in subjects with no history of pre-treatment and recent HIV diagnosis. In total, 78 antiretroviral treatment (ART)-naïve subjects with a recent HIV diagnosis were selected and followed by CD4+ T lymphocytes and viral load tests to detect virologic failure. We sequenced the basal samples retrospectively using next-generation sequencing (NGS), looking for low-frequency mutations that had not been detected before using the Sanger sequencing method (SSM) and describing the response to ART. Twenty-two subjects developed virologic failure (VF), and thirteen of them had at least one drug-resistance mutation associated with Reverse Transcriptase Inhibitors (RTI) and Protease Inhibitors (PIs) at frequency levels ≤ 1%, not detected previously in their basal genotyping test. No resistance mutations were observed to Integrase Strand Transfer Inhibitors (INSTIs). We identified a possible cause of VF in ART-naïve subjects with low-frequency mutations detected. To our knowledge, this is the first evaluation of pre-existing drug resistance for HIV-1 minority variants carried out on ART-naïve people living with HIV/AIDS (PLWHA) by analyzing the HIV-1 pol gene using NGS in the country.

Establishment of a multiplex polymerase chain reaction detection assay for three common harmful microalgae in the East China Sea

It is urgent to develop techniques that can simultaneously detect multiple microalgae, due to the diversity of harmful algal blooms (HABs)-forming algal species. The target algae species in this study are Heterosigma akashiwo, Prorocentrum donghaiense and Karenia mikimotoi. These algae are the dominant species that cause HABs in the East China Sea, and the multiple detection technique focusing on these three algae is not common. Therefore, this study established a multiplex polymerase chain reaction(mPCR) to diagnose the three algae, which is simple and low cost. First, the corresponding specific primers were designed based on the D1-D2 region of the large subunit (LSU) ribosomal DNA sequence. Then, mPCR was established and the reaction conditions were optimized. And the specificity, sensitivity, and stability of mPCR were evaluated. The result of specificity test showed that the established mPCR had good specificity for the target microalgae and did not cross-react with eighteen non-target microalgae. The sensitivity of experiment was 3.3 × 10^-1 ng μL^-1, and the established mPCR was not affected by the interfering microalgae. Moreover, the practicability evaluation of mPCR by using the simulated natural water samples showed that the detection limit of target microalgae was 100 cells mL^-1, which could meet the demand for early warning of HABs. In summary, the established mPCR is characterized by strong specificity, good stability, and multiple analysis to detect H. akashiwo, P. donghaiense, and K. mikimotoi.

Low-level viraemia among people living with HIV in Nigeria: a retrospective longitudinal cohort study

BACKGROUND

HIV transmission can occur with a viral load of at least 200 copies per mL of blood and low-level viraemia can lead to virological failure; the threshold level at which risk for virological failure is conferred is uncertain. To better understand low-level viraemia prevalence and outcomes, we analysed retrospective longitudinal data from a large cohort of people living with HIV on antiretroviral therapy (ART) in Nigeria.

METHODS

In this retrospective cohort study using previously collected longitudinal patient data, we estimated rates of virological suppression (≤50 copies per mL), low-level viraemia (51-999 copies per mL), virological non-suppression (≥1000 copies per mL), and virological failure (≥2 consecutive virological non-suppression results) among people living with HIV aged 18 years and older who initiated and received at least 24 weeks of ART at 1005 facilities in 18 Nigerian states. We analysed risk for low-level viraemia, virological non-suppression, and virological failure using log-binomial regression and mixed-effects logistic regression.

FINDINGS

At first viral load for 402 668 patients during 2016-21, low-level viraemia was present in 64 480 (16·0%) individuals and virological non-suppression occurred in 46 051 (11·4%) individuals. Patients with low-level viraemia had increased risk of virological failure (adjusted relative risk 2·20, 95% CI 1·98-2·43; p<0·0001). Compared with patients with virological suppression, patients with low-level viraemia, even at 51-199 copies per mL, had increased odds of low-level viraemia and virological non-suppression at next viral load; patients on optimised ART (ie, integrase strand transfer inhibitors) had lower odds than those on non-integrase strand transfer inhibitors for the same low-level viraemia range (eg, viral load ≥1000 copies per mL following viral load 400-999 copies per mL, integrase strand transfer inhibitor: odds ratio 1·96, 95% CI 1·79-2·13; p<0·0001; non-integrase strand transfer inhibitor: 3·21, 2·90-3·55; p<0·0001).

INTERPRETATION

Patients with low-level viraemia had increased risk of virological non-suppression and failure. Programmes should revise monitoring benchmarks and targets from less than 1000 copies per mL to less than 50 copies per mL to strengthen clinical outcomes and track progress to epidemic control.

FUNDING

None.

Pathogen profiling of Australian rabbits by metatranscriptomic sequencing

Australia is known for its long history of using biocontrol agents, such as myxoma virus (MYXV) and rabbit haemorrhagic disease virus (RHDV), to manage wild European rabbit populations. Interestingly, while undertaking RHDV surveillance of rabbits that were found dead, we observed that approximately 40% of samples were negative for RHDV. To investigate whether other infectious agents are responsible for killing rabbits in Australia, we subjected a subset of these RHDV-negative liver samples to metatranscriptomic sequencing. In addition, we investigated whether the host transcriptome data could provide additional differentiation between likely infectious versus non-infectious causes of death. We identified transcripts from several Clostridia species, Pasteurella multocida, Pseudomonas spp., and Eimeria stiedae, in liver samples of several rabbits that had died suddenly, all of which are known to infect rabbits and are capable of causing disease and mortality. In addition, we identified Hepatitis E virus and Cyniclomyces yeast in some samples, both of which are not usually associated with severe disease. In one-third of the sequenced total liver RNAs, no infectious agent could be identified. While metatranscriptomic sequencing cannot provide definitive evidence of causation, additional host transcriptome analysis provided further insights to distinguish between pathogenic microbes and commensals or environmental contaminants. Interestingly, three samples where no pathogen could be identified showed evidence of up-regulated host immune responses, while immune response pathways were not up-regulated when E. stiedae, Pseudomonas, or yeast were detected. In summary, although no new putative rabbit pathogens were identified, this study provides a robust workflow for future investigations into rabbit mortality events.

Advances in multiplex molecular detection technologies for harmful algae

As the eutrophication of natural water bodies becomes more and more serious, the frequency of outbreaks of harmful algal blooms (HABs) mainly formed by harmful algae also increases. HABs have become a global ecological problem that poses a serious threat to human health and food safety. Therefore, it is extremely important to establish methods that can rapidly detect harmful algal species for early warning of HABs. The traditional morphology-based identification method is inefficient and inaccurate. In recent years, the rapid development of molecular biology techniques has provided new ideas for the detection of harmful algae and has become a research hotspot. The current molecular detection methods for harmful algal species mainly include fluorescence in situ hybridization, sandwich hybridization, and quantitative PCR (qPCR), but all of these methods can only detect single harmful algal species at a time. The establishment of methods for the simultaneous detection of multiple harmful algal species has become a new trend in the development of molecular detection technology because various harmful algal species may coexist in the natural water environment. The established molecular techniques for multiple detections of harmful algae mainly include gene chip, multiplex PCR, multiplex qPCR, massively parallel sequencing, antibody chip, and multiple isothermal amplification. This review mainly focuses on the principles, advantages and disadvantages, application progress, and application prospects of these multiple detection technologies, aiming at providing effective references not only for the fisheries but also for economic activities, environment, and human health.

Contamination Issue in Viral Metagenomics: Problems, Solutions, and Clinical Perspectives

We describe the most common internal and external sources and types of contamination encountered in viral metagenomic studies and discuss their negative impact on sequencing results, particularly for low-biomass samples and clinical applications. We also propose some basic recommendations for reducing the background noise in viral shotgun metagenomic (SM) studies, which would limit the bias introduced by various classes of contaminants. Regardless of the specific viral SM protocol, contamination cannot be totally avoided; in particular, the issue of reagent contamination should always be addressed with high priority. There is an urgent need for the development and validation of standards for viral metagenomic studies especially if viral SM protocols will be more widely applied in diagnostics.

Assessment of SARS-CoV-2 Genome Sequencing: Quality Criteria and Low-Frequency Variants

Although many laboratories worldwide have developed their sequencing capacities in response to the need for SARS-CoV-2 genome-based surveillance of variants, only a few reported some quality criteria to ensure sequence quality before lineage assignment and submission to public databases. Hence, we aimed here to provide simple quality control criteria for SARS-CoV-2 sequencing to prevent erroneous interpretation of low-quality or contaminated data. We retrospectively investigated 647 SARS-CoV-2 genomes obtained over 10 tiled amplicons sequencing runs. We extracted 26 potentially relevant metrics covering the entire workflow from sample selection to bioinformatics analysis. Based on data distribution, critical values were established for 11 selected metrics to prompt further quality investigations for problematic samples, in particular those with a low viral RNA quantity. Low-frequency variants (<70% of supporting reads) can result from PCR amplification errors, sample cross contaminations, or presence of distinct SARS-CoV2 genomes in the sample sequenced. The number and the prevalence of low-frequency variants can be used as a robust quality criterion to identify possible sequencing errors or contaminations. Overall, we propose 11 metrics with fixed cutoff values as a simple tool to evaluate the quality of SARS-CoV-2 genomes, among which are cycle thresholds, mean depth, proportion of genome covered at least 10×, and the number of low-frequency variants combined with mutation prevalence data.

From Sequence Data to Patient Result: A Solution for HIV Drug Resistance Genotyping With Exatype, End to End Software for Pol-HIV-1 Sanger Based Sequence Analysis and Patient HIV Drug Resistance Result Generation

Introduction:

With the rapid scale-up of antiretroviral therapy (ART) to treat HIV infection, there are ongoing concerns regarding probable emergence and transmission of HIV drug resistance (HIVDR) mutations. This scale-up has to lead to an increased need for routine HIVDR testing to inform the clinical decision on a regimen switch. Although the majority of wet laboratory processes are standardized, slow, labor-intensive data transfer and subjective manual sequence interpretation steps are still required to finalize and release patient results. We thus set out to validate the applicability of a software package to generate HIVDR patient results from raw sequence data independently.

Methods:

We assessed the performance characteristics of Hyrax Bioscience’s Exatype (a sequence data to patient result, fully automated sequence analysis software, which consolidates RECall, MEGA X and the Stanford HIV database) against the standard method (RECall and Stanford database). Exatype is a web-based HIV Drug resistance bioinformatic pipeline available at sanger.exatype.com. To validate the exatype, we used a test set of 135 remnant HIV viral load samples at the National HIV Reference Laboratory (NHRL).

Result:

We analyzed, and successfully generated results of 126 sequences out of 135 specimens by both Standard and Exatype software. Result production using Exatype required minimal hands-on time in comparison to the Standard (6 computation-hours using the standard method versus 1.5 Exatype computation-hours). Concordance between the 2 systems was 99.8% for 311,227 bases compared. 99.7% of the 0.2% discordant bases, were attributed to nucleotide mixtures as a result of the sequence editing in Recall. Both methods identified similar (99.1%) critical antiretroviral resistance-associated mutations resulting in a 99.2% concordance of resistance susceptibility interpretations. The Base-calling comparison between the 2 methods had Cohen’s kappa (0.97 to 0.99), implying an almost perfect agreement with minimal base calling variation. On a predefined dataset, RECall editing displayed the highest probability to score mixtures accurately 1 vs. 0.71 and the lowest chance to inaccurately assign mixtures to pure nucleotides (0.002–0.0008). This advantage is attributable to the manual sequence editing in RECall.

Conclusion:

The reduction in hands-on time needed is a benefit when using the Exatype HIV DR sequence analysis platform and result generation tool. There is a minimal difference in base calling between Exatype and standard methods. Although the discrepancy has minimal impact on drug resistance interpretation, allowance of sequence editing in Exatype as RECall can significantly improve its performance.

Molecular epidemiology in the HIV and SARS-CoV-2 pandemics

PURPOSE OF REVIEW

The aim of this review was to compare and contrast the application of molecular epidemiology approaches for the improved management and understanding of the HIV versus SARS-CoV-2 epidemics.

RECENT FINDINGS

Molecular biology approaches, including PCR and whole genome sequencing (WGS), have become powerful tools for epidemiological investigation. PCR approaches form the basis for many high-sensitivity diagnostic tests and can supplement traditional contact tracing and surveillance strategies to define risk networks and transmission patterns. WGS approaches can further define the causative agents of disease, trace the origins of the pathogen, and clarify routes of transmission. When coupled with clinical datasets, such as electronic medical record data, these approaches can investigate co-correlates of disease and pathogenesis. In the ongoing HIV epidemic, these approaches have been effectively deployed to identify treatment gaps, transmission clusters and risk factors, though significant barriers to rapid or real-time implementation remain critical to overcome. Likewise, these approaches have been successful in addressing some questions of SARS-CoV-2 transmission and pathogenesis, but the nature and rapid spread of the virus have posed additional challenges.

SUMMARY

Overall, molecular epidemiology approaches offer unique advantages and challenges that complement traditional epidemiological tools for the improved understanding and management of epidemics.

Resistance profile of HIV-1 quasispecies in patients under treatment failure using single molecule, real-time sequencing

OBJECTIVE

Short-read next-generation sequencing (NGS) has been implemented to study the resistance profile of HIV as it provides a higher sensitivity than Sanger sequencing. However, short-reads only generates a consensus view of the viral population rather than a reconstruction of the viral haplotypes. In this study, we evaluated the resistance profile of HIV quasispecies in patients undergoing treatment failure using SMRT sequencing.

DESIGN

Whole-pol RT-PCR was performed on viral RNA extracted from plasma samples of 38 HIV-positive individuals undergoing treatment failure, and sequenced in the RSII instrument. Error correction and viral haplotype phasing was performed with the Multilayer Directed Phasing and Sequencing (MDPSeq) algorithm. Presence of resistance mutations reported by the IAS-USA in 2017 was assessed using an in-house script.

RESULTS

The SMRT sequencing-based test detected 131/134 resistance mutations previously detected using a Sanger sequencing-based test. However, the SMRT test also identified seven additional mutations present at an estimated frequency lower than 30%. The intra-host phylogenetic analysis showed that seven samples harbored at least one resistance variant at 20--80% frequency. The haplotype-resolved sequencing revealed viral diversification and selection of new resistance during suboptimal treatment, an overall trend toward selection and accumulation of new resistance mutations, as well as the co-existence of resistant and susceptible variants.

CONCLUSION

Our results validate the SMRT sequencing-based test for detection of HIV drug resistance. In addition, this method unraveled the complex dynamic of HIV quasispecies during treatment failure, which might have several implications on clinical management.

Short Communication: HIV-DRLink: A Tool for Reporting Linked HIV-1 Drug Resistance Mutations in Large Single-Genome Data Sets Using the Stanford HIV Database

The prevalence of HIV-1 drug resistance is increasing worldwide and monitoring its emergence is important for the successful management of populations receiving combination antiretroviral therapy. It is likely that pre-existing drug resistance mutations linked on the same viral genomes are predictive of treatment failure. Because of the large number of sequences generated by ultrasensitive single-genome sequencing (uSGS) and other similar next-generation sequencing methods, it is difficult to assess each sequence individually for linked drug resistance mutations. Several software/programs exist to report the frequencies of individual mutations in large data sets, but they provide no information on linkage of resistance mutations. In this study, we report the HIV-DRLink program, a research tool that provides resistance mutation frequencies as well as their genetic linkage by parsing and summarizing the Sierra output from the Stanford HIV Database. The HIV-DRLink program should only be used on data sets generated by methods that eliminate artifacts due to polymerase chain reaction recombination, for example, standard single-genome sequencing or uSGS. HIV-DRLink is exclusively a research tool and is not intended to inform clinical decisions.

Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework

Next-generation sequencing (NGS) in HIV drug resistance (HIVDR) testing has the potential to improve both clinical and public health settings, however it challenges the normal operations of quality management systems to be more flexible due to its complexity, massive data generation, and rapidly evolving protocols. While guidelines for quality management in NGS data have previously been outlined, little guidance has been implemented for NGS-based HIVDR testing. This document summarizes quality control procedures for NGS-based HIVDR testing laboratories using a laboratory information systems (LIS) framework. Here, we focus in particular on the quality control measures applied on the final sequencing product aligned with the recommendations from the World Health Organization HIV Drug Resistance Laboratory Network.

Measurement error and variant-calling in deep Illumina sequencing of HIV

MOTIVATION

Next-generation deep sequencing of viral genomes, particularly on the Illumina platform, is increasingly applied in HIV research. Yet, there is no standard protocol or method used by the research community to account for measurement errors that arise during sample preparation and sequencing. Correctly calling high and low-frequency variants while controlling for erroneous variants is an important precursor to downstream interpretation, such as studying the emergence of HIV drug-resistance mutations, which in turn has clinical applications and can improve patient care.

RESULTS

We developed a new variant-calling pipeline, hivmmer, for Illumina sequences from HIV viral genomes. First, we validated hivmmer by comparing it to other variant-calling pipelines on real HIV plasmid datasets. We found that hivmmer achieves a lower rate of erroneous variants, and that all methods agree on the frequency of correctly called variants. Next, we compared the methods on an HIV plasmid dataset that was sequenced using Primer ID, an amplicon-tagging protocol, which is designed to reduce errors and amplification bias during library preparation. We show that the Primer ID consensus exhibits fewer erroneous variants compared to the variant-calling pipelines, and that hivmmer more closely approaches this low error rate compared to the other pipelines. The frequency estimates from the Primer ID consensus do not differ significantly from those of the variant-calling pipelines.

AVAILABILITY AND IMPLEMENTATION

hivmmer is freely available for non-commercial use from https://github.com/kantorlab/hivmmer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Next-Generation Sequencing for HIV Drug Resistance Testing: Laboratory, Clinical, and Implementation Considerations

Higher accessibility and decreasing costs of next generation sequencing (NGS), availability of commercial kits, and development of dedicated analysis pipelines, have allowed an increasing number of laboratories to adopt this technology for HIV drug resistance (HIVDR) genotyping. Conventional HIVDR genotyping is traditionally carried out using population-based Sanger sequencing, which has a limited capacity for reliable detection of variants present at intra-host frequencies below a threshold of approximately 20%. NGS has the potential to improve sensitivity and quantitatively identify low-abundance variants, improving efficiency and lowering costs. However, some challenges exist for the standardization and quality assurance of NGS-based HIVDR genotyping. In this paper, we highlight considerations of these challenges as related to laboratory, clinical, and implementation of NGS for HIV drug resistance testing. Several sources of variation and bias occur in each step of the general NGS workflow, i.e., starting material, sample type, PCR amplification, library preparation method, instrument and sequencing chemistry-inherent errors, and data analysis options and limitations. Additionally, adoption of NGS-based HIVDR genotyping, especially for clinical care, poses pressing challenges, especially for resource-poor settings, including infrastructure and equipment requirements and cost, logistic and supply chains, instrument service availability, personnel training, validated laboratory protocols, and standardized analysis outputs. The establishment of external quality assessment programs may help to address some of these challenges and is needed to proceed with NGS-based HIVDR genotyping adoption.

External Quality Assessment for Next-Generation Sequencing-Based HIV Drug Resistance Testing: Unique Requirements and Challenges

Over the past decade, there has been an increase in the adoption of next generation sequencing (NGS) technologies for HIV drug resistance (HIVDR) testing. NGS far outweighs conventional Sanger sequencing as it has much higher throughput, lower cost when samples are batched and, most importantly, significantly higher sensitivities for variants present at low frequencies, which may have significant clinical implications. Despite the advantages of NGS, Sanger sequencing remains the gold standard for HIVDR testing, largely due to the lack of standardization of NGS-based HIVDR testing. One important aspect of standardization includes external quality assessment (EQA) strategies and programs. Current EQA for Sanger-based HIVDR testing includes proficiency testing where samples are sent to labs and the performance of the lab conducting such assays is evaluated. The current methods for Sanger-based EQA may not apply to NGS-based tests because of the fundamental differences in their technologies and outputs. Sanger-based genotyping reports drug resistance mutations (DRMs) data as dichotomous, whereas NGS-based HIVDR genotyping also reports DRMs as numerical data (percent abundance). Here we present an overview of the need to develop EQA for NGS-based HIVDR testing and some unique challenges that may be encountered.

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

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.

Role of co-expressed APOBEC3F and APOBEC3G in inducing HIV-1 drug resistance

The APOBEC3 enzymes can induce mutagenesis of HIV-1 proviral DNA through the deamination of cytosine. HIV-1 overcomes this restriction through the viral protein Vif that induces APOBEC3 proteasomal degradation. Within this dynamic host-pathogen relationship, the APOBEC3 enzymes have been found to be beneficial, neutral, or detrimental to HIV-1 biology. Here, we assessed the ability of co-expressed APOBEC3F and APOBEC3G to induce HIV-1 resistance to antiviral drugs. We found that co-expression of APOBEC3F and APOBEC3G enabled partial resistance of APOBEC3F to Vif-mediated degradation with a corresponding increase in APOBEC3F-induced deaminations in the presence of Vif, in addition to APOBEC3G-induced deaminations. We recovered HIV-1 drug resistant variants resulting from APOBEC3-induced mutagenesis, but these variants were less able to replicate than drug resistant viruses derived from RT-induced mutations alone. The data support a model in which APOBEC3 enzymes cooperate to restrict HIV-1, promoting viral inactivation over evolution to drug resistance.

Dengue drug discovery: Progress, challenges and outlook

In the context of the only available vaccine (DENGVAXIA) that was marketed in several countries, but poses higher risks to unexposed individuals, the development of antivirals for dengue virus (DENV), whilst challenging, would bring significant benefits to public health. Here recent progress in the field of DENV drug discovery made in academic laboratories and industry is reviewed. Characteristics of an ideal DENV antiviral molecule, given the specific immunopathology provoked by this acute viral infection, are described. New chemical classes identified from biochemical, biophysical and phenotypic screens that target viral (especially NS4B) and host proteins, offer promising opportunities for further development. In particular, new methodologies ("omics") can accelerate the discovery of much awaited flavivirus specific inhibitors. Challenges and opportunities in lead identification activities as well as the path to clinical development of dengue drugs are discussed. To galvanize DENV drug discovery, collaborative public-public partnerships and open-access resources will greatly benefit both the DENV research community and DENV patients.

Bioinformatic data processing pipelines in support of next-generation sequencing-based HIV drug resistance testing: the Winnipeg Consensus

INTRODUCTION

Next-generation sequencing (NGS) has several advantages over conventional Sanger sequencing for HIV drug resistance (HIVDR) genotyping, including detection and quantitation of low-abundance variants bearing drug resistance mutations (DRMs). However, the high HIV genomic diversity, unprecedented large volume of data, complexity of analysis and potential for error pose significant challenges for data processing. Several NGS analysis pipelines have been developed and used in HIVDR research; however, the absence of uniformity in data processing strategies results in lack of consistency and comparability of outputs from different pipelines. To fill this gap, an international symposium on bioinformatic strategies for NGS-based HIVDR testing was held in February 2018 in Winnipeg, Canada, convening laboratory scientists, bioinformaticians and clinicians involved in four recently developed, publicly available NGS HIVDR pipelines. The goal of this symposium was to establish a consensus on effective bioinformatic strategies for NGS data management and its use for HIVDR reporting.

DISCUSSION

Essential functionalities of an NGS HIVDR pipeline were divided into five analytic blocks: (1) NGS read quality control (QC)/quality assurance (QA); (2) NGS read alignment and reference mapping; (3) HIV variant calling and variant QC; (4) NGS HIVDR reporting; and (5) extended data applications and additional considerations for data management. The consensuses reached among the participants on all major aspects of these blocks are summarized here. They encompass not only recommended data management and analysis strategies, but also detailed bioinformatic approaches that help ensure accuracy of the derived HIVDR analysis outputs for both research and potential clinical use.

CONCLUSIONS

While NGS is being adopted more broadly in HIVDR testing laboratories, data processing is often a bottleneck hindering its generalized application. The proposed standardization of NGS read QC/QA, read alignment and reference mapping, variant calling and QC, HIVDR reporting and relevant data management strategies in this "Winnipeg Consensus" may serve as a starting guideline for NGS HIVDR data processing that informs the refinement of existing pipelines and those yet to be developed. Moreover, the bioinformatic strategies presented here may apply more broadly to NGS data analysis of microbes harbouring significant genomic diversity.

HIV-1 full-genome phylogenetics of generalized epidemics in sub-Saharan Africa: impact of missing nucleotide characters in next-generation sequences

To characterize HIV-1 transmission dynamics in regions where the burden of HIV-1 is greatest, the “Phylogenetics and Networks for Generalised HIV Epidemics in Africa” consortium (PANGEA-HIV) is sequencing full-genome viral isolates from across sub-Saharan Africa. We report the first 3,985 PANGEA-HIV consensus sequences from four cohort sites (Rakai Community Cohort Study, n = 2,833; MRC/UVRI Uganda, n = 701; Mochudi Prevention Project, n = 359; Africa Health Research Institute Resistance Cohort, n = 92). Next-generation sequencing success rates varied: more than 80% of the viral genome from the gag to the nef genes could be determined for all sequences from South Africa, 75% of sequences from Mochudi, 60% of sequences from MRC/UVRI Uganda, and 22% of sequences from Rakai. Partial sequencing failure was primarily associated with low viral load, increased for amplicons closer to the 3′ end of the genome, was not associated with subtype diversity except HIV-1 subtype D, and remained significantly associated with sampling location after controlling for other factors. We assessed the impact of the missing data patterns in PANGEA-HIV sequences on phylogeny reconstruction in simulations. We found a threshold in terms of taxon sampling below which the patchy distribution of missing characters in next-generation sequences (NGS) has an excess negative impact on the accuracy of HIV-1 phylogeny reconstruction, which is attributable to tree reconstruction artifacts that accumulate when branches in viral trees are long. The large number of PANGEA-HIV sequences provides unprecedented opportunities for evaluating HIV-1 transmission dynamics across sub-Saharan Africa and identifying prevention opportunities. Molecular epidemiological analyses of these data must proceed cautiously because sequence sampling remains below the identified threshold and a considerable negative impact of missing characters on phylogeny reconstruction is expected.