VirVarSeq: a low-frequency virus variant detection pipeline for Illumina sequencing using adaptive base-calling accuracy filtering

Mapping mutational fitness effects across the coxsackievirus B3 proteome reveals distinct profiles of mutation tolerability

RNA viruses have notoriously high mutation rates due to error-prone replication by their RNA polymerase. However, natural selection concentrates variability in a few key viral proteins. To test whether this stems from different mutation tolerance profiles among viral proteins, we measured the effect of >40,000 non-synonymous mutations across the full proteome of coxsackievirus B3 as well as >97% of all possible codon deletions in the nonstructural proteins. We find significant variation in mutational tolerance within and between individual viral proteins, which correlated with both general and protein-specific structural and functional attributes. Furthermore, mutational fitness effects remained stable across cell lines, suggesting selection pressures are mostly conserved across environments. In addition to providing a rich dataset for understanding virus biology and evolution, our results illustrate that incorporation of mutational tolerance data into druggable pocket discovery can aid in selecting targets with high barriers to drug resistance.

Rate of response to initial antiretroviral therapy according to level of pre-existing HIV-1 drug resistance detected by next-generation sequencing in the strategic timing of antiretroviral treatment (START) study

OBJECTIVES

The main objective of this analysis was to evaluate the impact of pre-existing drug resistance by next-generation sequencing (NGS) on the risk of treatment failure (TF) of first-line regimens in participants enrolled in the START study.

METHODS

Stored plasma from participants with entry HIV RNA >1000 copies/mL were analysed using NGS (llumina MiSeq). Pre-existing drug resistance was defined using the mutations considered by the Stanford HIV Drug Resistance Database (HIVDB v8.6) to calculate the genotypic susceptibility score (GSS, estimating the number of active drugs) for the first-line regimen at the detection threshold windows of >20%, >5%, and >2% of the viral population. Survival analysis was conducted to evaluate the association between the GSS and risk of TF (viral load >200 copies/mL plus treatment change).

RESULTS

Baseline NGS data were available for 1380 antiretroviral therapy (ART)-naïve participants enrolled over 2009-2013. First-line ART included a non-nucleoside reverse transcriptase inhibitor (NNRTI) in 976 (71%), a boosted protease inhibitor in 297 (22%), or an integrase strand transfer inhibitor in 107 (8%). The proportions of participants with GSS <3 were 7% for >20%, 10% for >5%, and 17% for the >2% thresholds, respectively. The adjusted hazard ratio of TF associated with a GSS of 0-2.75 versus 3 in the subset of participants with mutations detected at the >2% threshold was 1.66 (95% confidence interval 1.01-2.74; p = 0.05) and 2.32 (95% confidence interval 1.32-4.09; p = 0.003) after restricting the analysis to participants who started an NNRTI-based regimen.

CONCLUSIONS

Up to 17% of participants initiated ART with a GSS <3 on the basis of NGS data. Minority variants were predictive of TF, especially for participants starting NNRTI-based regimens.

Molecular and Structural Aspects of Clinically Relevant Mutations of SARS-CoV-2 RNA-Dependent RNA Polymerase in Remdesivir-Treated Patients

(1) Background: SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target to fight COVID-19, and many RdRp inhibitors nucleotide/nucleoside analogs, such as remdesivir, have been identified or are in clinical studies. However, the appearance of resistant mutations could reduce their efficacy. In the present work, we structurally evaluated the impact of RdRp mutations found at baseline in 39 patients treated with remdesivir and associated with a different degree of antiviral response in vivo. (2) Methods: A refined bioinformatics approach was applied to assign SARS-CoV-2 clade and lineage, and to define RdRp mutational profiles. In line with such a method, the same mutations were built and analyzed by combining docking and thermodynamics evaluations with both molecular dynamics and representative pharmacophore models. (3) Results: Clinical studies revealed that patients bearing the most prevalent triple mutant P323L+671S+M899I, which was present in 41% of patients, or the more complex mutational profile P323L+G671S+L838I+D738Y+K91E, which was found with a prevalence of 2.6%, showed a delayed reduced response to remdesivir, as confirmed by the increase in SARS-CoV-2 viral load and by a reduced theoretical binding affinity versus RdRp (ΔGbindWT = -122.70 kcal/mol; ΔGbindP323L+671S+M899I = -84.78 kcal/mol; ΔGbindP323L+G671S+L838I+D738Y+K91E = -96.74 kcal/mol). Combined computational approaches helped to rationalize such clinical observations, offering a mechanistic understanding of the allosteric effects of mutants on the global motions of the viral RNA synthesis machine and in the changes of the interactions patterns of remdesivir during its binding.

Frequency of Atypical Mutations in the Spike Glycoprotein in SARS-CoV-2 Circulating from July 2020 to July 2022 in Central Italy: A Refined Analysis by Next Generation Sequencing

In this study, we provided a retrospective overview in order to better define SARS-CoV-2 variants circulating in Italy during the first two years of the pandemic, by characterizing the spike mutational profiles and their association with viral load (expressed as ct values), N-glycosylation pattern, hospitalization and vaccination. Next-generation sequencing (NGS) data were obtained from 607 individuals (among them, 298 vaccinated and/or 199 hospitalized). Different rates of hospitalization were observed over time and among variants of concern (VOCs), both in the overall population and in vaccinated individuals (Alpha: 40.7% and 31.3%, Beta: 0%, Gamma: 36.5% and 44.4%, Delta: 37.8% and 40.2% and Omicron: 11.2% and 7.1%, respectively, both p-values < 0.001). Approximately 32% of VOC-infected individuals showed at least one atypical major spike mutation (intra-prevalence > 90%), with a distribution differing among the strains (22.9% in Alpha, 14.3% in Beta, 41.8% in Gamma, 46.5% in Delta and 15.4% in Omicron, p-value < 0.001). Overall, significantly less atypical variability was observed in vaccinated individuals than unvaccinated individuals; nevertheless, vaccinated people who needed hospitalization showed an increase in atypical variability compared to vaccinated people that did not need hospitalization. Only 5/607 samples showed a different putative N-glycosylation pattern, four within the Delta VOC and one within the Omicron BA.2.52 sublineage. Interestingly, atypical minor mutations (intra-prevalence < 20%) were associated with higher Ct values and a longer duration of infection. Our study reports updated information on the temporal circulation of SARS-CoV-2 variants circulating in Central Italy and their association with hospitalization and vaccination. The results underline how SARS-CoV-2 has changed over time and how the vaccination strategy has contributed to reducing severity and hospitalization for this infection in Italy.

Molecular Determinants of Mouse Adaptation of Rat Hepacivirus

The lack of robust immunocompetent animal models for hepatitis C virus (HCV) impedes vaccine development and studies of immune responses. Norway rat hepacivirus (NrHV) infection in rats shares HCV-defining characteristics, including hepatotropism, chronicity, immune responses, and aspects of liver pathology. To exploit genetic variants and research tools, we previously adapted NrHV to prolonged infection in laboratory mice. Through intrahepatic RNA inoculation of molecular clones of the identified variants, we here characterized four mutations in the envelope proteins responsible for mouse adaptation, including one disrupting a glycosylation site. These mutations led to high-titer viremia, similar to that observed in rats. In 4-week-old mice, infection was cleared after around 5 weeks compared to 2 to 3 weeks for nonadapted virus. In contrast, the mutations led to persistent but attenuated infection in rats, and they partially reverted, accompanied by an increase in viremia. Attenuated infection in rat but not mouse hepatoma cells demonstrated that the characterized mutations were indeed mouse adaptive rather than generally adaptive across species and that species determinants and not immune interactions were responsible for attenuation in rats. Unlike persistent NrHV infection in rats, acute resolving infection in mice was not associated with the development of neutralizing antibodies. Finally, infection of scavenger receptor B-I (SR-BI) knockout mice suggested that adaptation to mouse SR-BI was not a primary function of the identified mutations. Rather, the virus may have adapted to lower dependency on SR-BI, thereby potentially surpassing species-specific differences. In conclusion, we identified specific determinants of NrHV mouse adaptation, suggesting species-specific interactions during entry. IMPORTANCE A prophylactic vaccine is required to achieve the World Health Organization's objective for hepatitis C virus elimination as a serious public health threat. However, the lack of robust immunocompetent animal models supporting hepatitis C virus infection impedes vaccine development as well as studies of immune responses and viral evasion. Hepatitis C virus-related hepaciviruses were discovered in a number of animal species and provide useful surrogate infection models. Norway rat hepacivirus is of particular interest, as it enables studies in rats, an immunocompetent and widely used small laboratory animal model. Its adaptation to robust infection also in laboratory mice provides access to a broader set of mouse genetic lines and comprehensive research tools. The presented mouse-adapted infectious clones will be of utility for reverse genetic studies, and the Norway rat hepacivirus mouse model will facilitate studies of hepacivirus infection for in-depth characterization of virus-host interactions, immune responses, and liver pathology.

Blocking NS3-NS4B interaction inhibits dengue virus in non-human primates

Dengue is a major health threat and the number of symptomatic infections caused by the four dengue serotypes is estimated to be 96 million1 with annually around 10,000 deaths2. However, no antiviral drugs are available for the treatment or prophylaxis of dengue. We recently described the interaction between non-structural proteins NS3 and NS4B as a promising target for the development of pan-serotype dengue virus (DENV) inhibitors3. Here we present JNJ-1802-a highly potent DENV inhibitor that blocks the NS3-NS4B interaction within the viral replication complex. JNJ-1802 exerts picomolar to low nanomolar in vitro antiviral activity, a high barrier to resistance and potent in vivo efficacy in mice against infection with any of the four DENV serotypes. Finally, we demonstrate that the small-molecule inhibitor JNJ-1802 is highly effective against viral infection with DENV-1 or DENV-2 in non-human primates. JNJ-1802 has successfully completed a phase I first-in-human clinical study in healthy volunteers and was found to be safe and well tolerated4. These findings support the further clinical development of JNJ-1802, a first-in-class antiviral agent against dengue, which is now progressing in clinical studies for the prevention and treatment of dengue.

Deep-sequencing of viral genomes from a large and diverse cohort of treatment-naive HIV-infected persons shows associations between intrahost genetic diversity and viral load

BACKGROUND

Infection with human immunodeficiency virus type 1 (HIV) typically results from transmission of a small and genetically uniform viral population. Following transmission, the virus population becomes more diverse because of recombination and acquired mutations through genetic drift and selection. Viral intrahost genetic diversity remains a major obstacle to the cure of HIV; however, the association between intrahost diversity and disease progression markers has not been investigated in large and diverse cohorts for which the majority of the genome has been deep-sequenced. Viral load (VL) is a key progression marker and understanding of its relationship to viral intrahost genetic diversity could help design future strategies for HIV monitoring and treatment.

METHODS

We analysed deep-sequenced viral genomes from 2,650 treatment-naive HIV-infected persons to measure the intrahost genetic diversity of 2,447 genomic codon positions as calculated by Shannon entropy. We tested for associations between VL and amino acid (AA) entropy accounting for sex, age, race, duration of infection, and HIV population structure.

RESULTS

We confirmed that the intrahost genetic diversity is highest in the env gene. Furthermore, we showed that mean Shannon entropy is significantly associated with VL, especially in infections of >24 months duration. We identified 16 significant associations between VL (p-value<2.0x10-5) and Shannon entropy at AA positions which in our association analysis explained 13% of the variance in VL. Finally, equivalent analysis based on variation in HIV consensus sequences explained only 2% of VL variance.

CONCLUSIONS

Our results elucidate that viral intrahost genetic diversity is associated with VL and could be used as a better disease progression marker than HIV consensus sequence variants, especially in infections of longer duration. We emphasize that viral intrahost diversity should be considered when studying viral genomes and infection outcomes.

TRIAL REGISTRATION

Samples included in this study were derived from participants who consented in the clinical trial, START (NCT00867048) (23), run by the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT). All the participant sites are listed here: http://www.insight-trials.org/start/my_phpscript/participating.php?by=site.

A general approach to identify low-frequency variants within influenza samples collected during routine surveillance

Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 10⁴ genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016-2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.

A pan-serotype dengue virus inhibitor targeting the NS3-NS4B interaction

Dengue virus causes approximately 96 million symptomatic infections annually, manifesting as dengue fever or occasionally as severe dengue^1,2. There are no antiviral agents available to prevent or treat dengue. Here, we describe a highly potent dengue virus inhibitor (JNJ-A07) that exerts nanomolar to picomolar activity against a panel of 21 clinical isolates that represent the natural genetic diversity of known genotypes and serotypes. The molecule has a high barrier to resistance and prevents the formation of the viral replication complex by blocking the interaction between two viral proteins (NS3 and NS4B), thus revealing a previously undescribed mechanism of antiviral action. JNJ-A07 has a favourable pharmacokinetic profile that results in outstanding efficacy against dengue virus infection in mouse infection models. Delaying start of treatment until peak viraemia results in a rapid and significant reduction in viral load. An analogue is currently in further development.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

Human immunotypes impose selection on viral genotypes through viral epitope specificity

BACKGROUND

Understanding the genetic interplay between human hosts and infectious pathogens is crucial for how we interpret virulence factors. Here, we tested for associations between HIV and host genetics, and interactive genetic effects on viral load (VL) in HIV+ ART-naive clinical trial participants.

METHODS

HIV genomes were sequenced and the encoded amino acid (AA) variants were associated with VL, human single nucleotide polymorphisms (SNPs) and imputed HLA alleles, using generalized linear models with Bonferroni correction.

RESULTS

Human (388,501 SNPs) and HIV (3,010 variants) genetic data was available for 2,122 persons. Four HIV variants were associated with VL (p-values<1.66×10 -5). Twelve HIV variants were associated with a range of 1-512 human SNPs (p-value<4.28×10 -11). We found 46 associations between HLA alleles and HIV variants (p-values<1.29×10 -7). We found HIV variants and immunotypes when analyzed separately, were associated with lower VL, whereas the opposite was true when analyzed in concert. Epitope binding prediction showed HLA alleles to be weaker binders of associated HIV AA variants relative to alternative variants on the same position.

CONCLUSIONS

Our results show the importance of immunotype specificity on viral antigenic determinants, and the identified genetic interplay puts emphasis that viral and human genetics should be studied in the context of each other.

Transmitted HIV-1 drug resistance in a large international cohort using next-generation sequencing: results from the Strategic Timing of Antiretroviral Treatment (START) study

OBJECTIVES

The aim of this analysis was to characterize transmitted drug resistance (TDR) in Strategic Timing of Antiretroviral Treatment (START) study participants by next-generation sequencing (NGS), a sensitive assay capable of detecting low-frequency variants.

METHODS

Stored plasma from participants with entry HIV RNA > 1000 copies/mL were analysed by NGS (Illumina MiSeq). TDR was based on the WHO 2009 surveillance definition with the addition of reverse transcriptase (RT) mutations T215N and E138K, and integrase strand transfer inhibitor (INSTI) surveillance mutations (Stanford HIVdb). Drug resistance mutations (DRMs) detected at three thresholds are reported: > 2%, 5% and 20% of the viral population.

RESULTS

Between 2009 and 2013, START enrolled 4684 antiretroviral therapy (ART)-naïve individuals in 35 countries. Baseline NGS data at study entry were available for 2902 participants. Overall prevalence rates of TDR using a detection threshold of 2%/5%/20% were 9.2%/5.6%/3.2% for nucleoside reverse transcriptase inhibitors (NRTIs), 9.2%/6.6%/4.9% for non-NRTIs, 11.4%/5.5%/2.4% for protease inhibitors (PIs) and 3.5%/1.6%/0.1% for INSTI DRMs and varied by geographic region. Using the 2% detection threshold, individual DRMs with the highest prevalence were: PI M46IL (5.5%), RT K103NS (3.5%), RT G190ASE (3.1%), T215ISCDVEN (2.5%), RT M41L (2.2%), RT K219QENR (1.7%) and PI D30N (1.6%). INSTI DRMs were detected almost exclusively below the 20% detection threshold, most commonly Y143H (0.4%), Q148R (0.4%) and T66I (0.4%).

CONCLUSIONS

Use of NGS in this study population resulted in the detection of a large proportion of low-level variants which would not have been detected by traditional Sanger sequencing. Global surveillance studies utilizing NGS should provide a more comprehensive assessment of TDR prevalence in different regions of the world.

Complete genome characterization of human noroviruses allows comparison of minor alleles during acute and chronic infections

Human noroviruses (HuNoVs) circulate globally, affect all age groups and place a substantial burden upon health services. High genetic diversity leading to antigenic variation plays a significant role in HuNoV epidemiology, driving periodic global emergence of epidemic variants. Studies have suggested that immunocompromised individuals may be a reservoir for such epidemic variants, but studies investigating the diversity and emergence of HuNoV variants in immunocompetent individuals are underrepresented. To address this, we sequenced the genomes of HuNoVs present in samples collected longitudinally from one immunocompetent (acute infection) and one immunocompromised (chronic infection) patient. A broadly reactive HuNoV capture-based method was used to concentrate the virus present in these specimens prior to massively parallel sequencing to recover near complete viral genomes. Using a novel bioinformatics pipeline, we demonstrated that persistent minor alleles were present in both acute and chronic infections, and that minor allele frequencies represented a larger proportion of the population during chronic infection. In acute infection, minor alleles were more evenly spread across the genome, although present at much lower frequencies, and therefore difficult to discern from error. By contrast, in the chronic infection, more minor alleles were present in the minor structural protein. No non-synonymous minor alleles were detected in the major structural protein over the short sampling period of the HuNoV chronic infection, suggesting where immune pressure is variable or non-existent, epidemic variants could emerge over longer periods of infection by random chance.

Intra-Host Diversity of SARS-Cov-2 Should Not Be Neglected: Case of the State of Victoria, Australia

Since the identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the etiological agent of the current COVID-19 pandemic, a rapid and massive effort has been made to obtain the genomic sequences of this virus to monitor (in near real time) the phylodynamic and diversity of this new pathogen. However, less attention has been given to the assessment of intra-host diversity. RNA viruses such as SARS-CoV-2 inhabit the host as a population of variants called quasispecies. We studied the quasispecies diversity in four of the main SARS-CoV-2 genes (ORF1a, ORF1b, S and N genes), using a dataset consisting of 210 next-generation sequencing (NGS) samples collected between January and early April of 2020 in the State of Victoria, Australia. We found evidence of quasispecies diversity in 68% of the samples, 76% of which was nonsynonymous variants with a higher density in the spike (S) glycoprotein and ORF1a genes. About one-third of the nonsynonymous intra-host variants were shared among the samples, suggesting host-to-host transmission. Quasispecies diversity changed over time. Phylogenetic analysis showed that some of the intra-host single-nucleotide variants (iSNVs) were restricted to specific lineages, highlighting their potential importance in the epidemiology of this virus. A greater effort must be made to determine the magnitude of the genetic bottleneck during transmission and the epidemiological and/or evolutionary factors that may play a role in the changes in the diversity of quasispecies over time.

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era

The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.

Globally defining the effects of mutations in a picornavirus capsid

The capsids of non-enveloped viruses are highly multimeric and multifunctional protein assemblies that play key roles in viral biology and pathogenesis. Despite their importance, a comprehensive understanding of how mutations affect viral fitness across different structural and functional attributes of the capsid is lacking. To address this limitation, we globally define the effects of mutations across the capsid of a human picornavirus. Using this resource, we identify structural and sequence determinants that accurately predict mutational fitness effects, refine evolutionary analyses, and define the sequence specificity of key capsid-encoded motifs. Furthermore, capitalizing on the derived sequence requirements for capsid-encoded protease cleavage sites, we implement a bioinformatic approach for identifying novel host proteins targeted by viral proteases. Our findings represent the most comprehensive investigation of mutational fitness effects in a picornavirus capsid to date and illuminate important aspects of viral biology, evolution, and host interactions.

A virus is made up of genetic material that is encased with a protective protein coat called the capsid. The capsid also helps the virus to infect host cells by binding to the host receptor proteins and releasing its genetic material. Inside the cell, the virus hitchhikes the infected cell’s machinery to grow or replicate its own genetic material.

Viral capsids are the main target of the host’s defence system, and therefore, continuously change in an attempt to escape the immune system by introducing alterations (known as mutations) into the genes encoding viral capsid proteins. Mutations occur randomly, and so while some changes to the viral capsid might confer an advantage, others may have no effect at all, or even weaken the virus.

To better understand the effect of capsid mutations on the virus’ ability to infect host cells, Mattenberger et al. studied the Coxsackievirus B3, which is linked to heart problems and acute heart failure in humans. The researchers analysed around 90% of possible amino acid mutations (over 14,800 mutations) and correlated each mutation to how it influenced the virus’ ability to replicate in human cells grown in the laboratory.

Based on these results, Mattenberger et al. developed a computer model to predict how a particular mutation might affect the virus. The analysis also identified specific amino acid sequences of capsid proteins that are essential for certain tasks, such as building the capsid. It also included an analysis of sequences in the capsid that allow it to be recognized by another viral protein, which cuts the capsid proteins into the right size from a larger precursor. By looking for similar sequences in human genes, the researchers identified several ones that the virus may attack and inactivate to support its own replication.

These findings may help identify potential drug targets to develop new antiviral therapies. For example, proteins of the capsid that are less likely to mutate will provide a better target as they lower the possibility of the virus to become resistant to the treatment. They also highlight new proteins in human cells that could potentially block the virus in cells.

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.

A Systematic Evaluation of High-Throughput Sequencing Approaches to Identify Low-Frequency Single Nucleotide Variants in Viral Populations

High-throughput sequencing such as those provided by Illumina are an efficient way to understand sequence variation within viral populations. However, challenges exist in distinguishing process-introduced error from biological variance, which significantly impacts our ability to identify sub-consensus single-nucleotide variants (SNVs). Here we have taken a systematic approach to evaluate laboratory and bioinformatic pipelines to accurately identify low-frequency SNVs in viral populations. Artificial DNA and RNA "populations" were created by introducing known SNVs at predetermined frequencies into template nucleic acid before being sequenced on an Illumina MiSeq platform. These were used to assess the effects of abundance and starting input material type, technical replicates, read length and quality, short-read aligner, and percentage frequency thresholds on the ability to accurately call variants. Analyses revealed that the abundance and type of input nucleic acid had the greatest impact on the accuracy of SNV calling as measured by a micro-averaged Matthews correlation coefficient score, with DNA and high RNA inputs (107 copies) allowing for variants to be called at a 0.2% frequency. Reduced input RNA (105 copies) required more technical replicates to maintain accuracy, while low RNA inputs (103 copies) suffered from consensus-level errors. Base errors identified at specific motifs identified in all technical replicates were also identified which can be excluded to further increase SNV calling accuracy. These findings indicate that samples with low RNA inputs should be excluded for SNV calling and reinforce the importance of optimising the technical and bioinformatics steps in pipelines that are used to accurately identify sequence variants.

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

Next-generation sequencing (NGS) has revolutionized the scale and depth of biomedical sciences. Because of its unique ability for the detection of sub-clonal variants within genetically diverse populations, NGS has been successfully applied to analyze and quantify the exceptionally-high diversity within viral quasispecies, and many low-frequency drug- or vaccine-resistant mutations of therapeutic importance have been discovered. Although many works have intensively discussed the latest NGS approaches and applications in general, none of them has focused on applying NGS in viral quasispecies studies, mostly due to the limited ability of current NGS technologies to accurately detect and quantify rare viral variants. Here, we summarize several error-correction strategies that have been developed to enhance the detection accuracy of minority variants. We also discuss critical considerations for preparing a sequencing library from viral RNAs and for analyzing NGS data to unravel the mutational landscape.

Resolution by deep sequencing of a dual hepatitis E virus infection transmitted via blood components

Hepatitis E virus (HEV) is a zoonotic infection, with consumption of processed pork products thought to be the major route of transmission in England. The clinical features of HEV infection range from asymptomatic infection to mild hepatitis to fulminant liver failure. Persistent, chronic hepatitis is increasingly recognized in immunocompromised patients. Infection via HEV-containing blood components and organs has been reported and measures to reduce this transmission risk were introduced into the blood service in England in 2016. We report here the sequence and phylogenetic findings from investigations into a transmission event from an HEV-infected donor to two recipients. Phylogenetic analysis of HEV genome sequence fragments obtained by Sanger sequencing showed that, whilst most of the sequences from both recipients' samples grouped with the sequence from the blood donor sample, the relationship of five sequences from recipient 2 were unresolved. Analysis of Illumina short-read deep sequence data demonstrated the presence of two divergent viral populations in the donor's sample that were also present in samples from both recipients. A clear phylogenetic relationship was established, indicating a probable transmission of both populations from the donor to each of the immunocompromised recipients. This study demonstrates the value of the application of new sequencing technologies combined with bioinformatic data analysis when Sanger sequencing is not able to clarify a proper phylogenetic relationship in the investigation of transmission events.

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.

Interpreting Viral Deep Sequencing Data with GLUE

Using deep sequencing technologies such as Illumina's platform, it is possible to obtain reads from the viral RNA population revealing the viral genome diversity within a single host. A range of software tools and pipelines can transform raw deep sequencing reads into Sequence Alignment Mapping (SAM) files. We propose that interpretation tools should process these SAM files, directly translating individual reads to amino acids in order to extract statistics of interest such as the proportion of different amino acid residues at specific sites. This preserves per-read linkage between nucleotide variants at different positions within a codon location. The samReporter is a subsystem of the GLUE software toolkit which follows this direct read translation approach in its processing of SAM files. We test samReporter on a deep sequencing dataset obtained from a cohort of 241 UK HCV patients for whom prior treatment with direct-acting antivirals has failed; deep sequencing and resistance testing have been suggested to be of clinical use in this context. We compared the polymorphism interpretation results of the samReporter against an approach that does not preserve per-read linkage. We found that the samReporter was able to properly interpret the sequence data at resistance-associated locations in nine patients where the alternative approach was equivocal. In three cases, the samReporter confirmed that resistance or an atypical substitution was present at NS5A position 30. In three further cases, it confirmed that the sofosbuvir-resistant NS5B substitution S282T was absent. This suggests the direct read translation approach implemented is of value for interpreting viral deep sequencing data.

Complete genome of DENV2 isolated from mosquitoes in Mexico

Dengue virus is the most prevalent arbovirus in Mexico, and although the diversity of this virus has been studied, the vast majority of sequences have been derived from viruses isolated from the human host. In this work, we aimed to sequence and to analyze DENVs derived from wild mosquitoes captured in Acapulco Guerrero, Mexico. We succeeded in determining three full genome sequences of such viruses and were able to compare them with other reported sequences from human and mosquito-derived DENVs. We found 15 nonsynonymous and 88 synonymous substitutions that were present more frequently in mosquito viruses than what would be expected by chance, although the limited number of genomes reported so far puts a constraint on the conclusions that can be derived from these analyses. Also, given the high depth of coverage attained in one of the genomes a variant analysis was carried out, finding 68 polymorphic sites in this genome. Interestingly, six of them corresponded to SNV that were detected as potentially differential between mosquitoes and humans, indicating that a that at least some positions may be maintained as polymorphic, which may facilitate host transmission.

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.

geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.

Easy and accurate reconstruction of whole HIV genomes from short-read sequence data with shiver

Studying the evolution of viruses and their molecular epidemiology relies on accurate viral sequence data, so that small differences between similar viruses can be meaningfully interpreted. Despite its higher throughput and more detailed minority variant data, next-generation sequencing has yet to be widely adopted for HIV. The difficulty of accurately reconstructing the consensus sequence of a quasispecies from reads (short fragments of DNA) in the presence of large between- and within-host diversity, including frequent indels, may have presented a barrier. In particular, mapping (aligning) reads to a reference sequence leads to biased loss of information; this bias can distort epidemiological and evolutionary conclusions. De novo assembly avoids this bias by aligning the reads to themselves, producing a set of sequences called contigs. However contigs provide only a partial summary of the reads, misassembly may result in their having an incorrect structure, and no information is available at parts of the genome where contigs could not be assembled. To address these problems we developed the tool shiver to pre-process reads for quality and contamination, then map them to a reference tailored to the sample using corrected contigs supplemented with the user's choice of existing reference sequences. Run with two commands per sample, it can easily be used for large heterogeneous data sets. We used shiver to reconstruct the consensus sequence and minority variant information from paired-end short-read whole-genome data produced with the Illumina platform, for sixty-five existing publicly available samples and fifty new samples. We show the systematic superiority of mapping to shiver's constructed reference compared with mapping the same reads to the closest of 3,249 real references: median values of 13 bases called differently and more accurately, 0 bases called differently and less accurately, and 205 bases of missing sequence recovered. We also successfully applied shiver to whole-genome samples of Hepatitis C Virus and Respiratory Syncytial Virus. shiver is publicly available from https://github.com/ChrisHIV/shiver.

Overview of Virus Metagenomic Classification Methods and Their Biological Applications

Metagenomics poses opportunities for clinical and public health virology applications by offering a way to assess complete taxonomic composition of a clinical sample in an unbiased way. However, the techniques required are complicated and analysis standards have yet to develop. This, together with the wealth of different tools and workflows that have been proposed, poses a barrier for new users. We evaluated 49 published computational classification workflows for virus metagenomics in a literature review. To this end, we described the methods of existing workflows by breaking them up into five general steps and assessed their ease-of-use and validation experiments. Performance scores of previous benchmarks were summarized and correlations between methods and performance were investigated. We indicate the potential suitability of the different workflows for (1) time-constrained diagnostics, (2) surveillance and outbreak source tracing, (3) detection of remote homologies (discovery), and (4) biodiversity studies. We provide two decision trees for virologists to help select a workflow for medical or biodiversity studies, as well as directions for future developments in clinical viral metagenomics.

Bovine viral diarrhea virus genomic variation within persistently infected cattle

Bovine viral diarrhea virus (BVDV) is a single stranded RNA virus in the family Flaviviridae that causes a form of persistent infection. If a fetus is infected in utero during the first 120days of gestation the resulting calf will be immunotolerant to the infecting strain and maintain the virus for life. These animals are epidemiologically important in maintaining BVDV on farms, but also present a unique opportunity to study quasispecies in vivo in the absence of significant selection by the host adaptive immune response. We used deep sequencing and novel analytical methods to characterize the viral populations within the mesenteric lymph nodes of 10 persistently infected animals. Our results indicate that the pattern of variability across the viral genome from animal to animal is very consistent within BVDV subgenotypes. However, the individual mutations that constitute this variation are not necessarily the same in each animal. Even in the absence of significant immune selection the structural genes of BVDV vary more extensively than the non-structural genes. These findings could be useful for future vaccine design against BVDV as well as for measuring and understanding patterns of variation in other ssRNA viruses, especially those that belong to the family Flaviviridae.

Next-Generation Human Immunodeficiency Virus Sequencing for Patient Management and Drug Resistance Surveillance

High-quality, simplified, and low-cost human immunodeficiency virus (HIV) drug resistance tests that are able to provide timely actionable HIV resistance data at individual, population, and programmatic levels are needed to confront the emerging drug-resistant HIV epidemic. Next-generation sequencing technologies embedded in automated cloud-computing analysis environments are ideally suited for such endeavor. Whereas NGS can reduce costs over Sanger sequencing, automated analysis pipelines make NGS accessible to molecular laboratories regardless of the available bioinformatic skills. They can also produce highly structured, high-quality data that could be examined by healthcare officials and program managers on a real-time basis to allow timely public health action. Here we discuss the opportunities and challenges of such an approach.

Short article: Retreatment of chronic hepatitis C virus infection after unsuccessful therapy with all-oral direct-acting antiviral regimens: a real-life experience

OBJECTIVE

Few real-life data are available on the retreatment of patients who failed direct-acting antiviral (DAA)-regimens. We reported the outcome of retreatment with approved DAA regimens in a real-life cohort of patients who previously failed an all-oral DAAs combination and we analyzed the association with resistance substitutions (RASs) performed at the time of virological failure.

AIM AND METHODS

Next-generation sequencing of the NS3, NS5A, and NS5B regions was performed by Illumina deep sequencing. The sequence reads were analyzed by an in-house pipeline.

RESULTS

Of the 16/759 (2%) patients who failed to achieve a sustained virological response at 12 weeks to all-oral DAAs from December 2014 to January 2016, 10 were retreated with licensed DAAs regimens. In all the patients, retreatment was followed by sustained virological response at 12 weeks. Baseline NS3-RASs before retreatment were observed in two patients who failed a sofosbuvir/simeprevir regimen: D168V RAS was detected in a genotype-4 patient, whereas the complex RAS-pattern Q80K, I170V, R155K, D168E was observed in a genotype-1a patient. Only one of the two patients who previously failed ombitasvir, paritaprevir/ritonavir, and dasabuvir underwent RAS analysis at relapse and showed baseline NS5A RAS (M28V) before retreatment.

CONCLUSION

These real-life findings indicated a high efficacy of sofosbuvir+NS5A-inihbitors in retreating NS3-experienced patients and also NS5A-experienced patients by using a 24-week course ribavirin-containing regimen. The relevance of hepatitis C virus resistance testing before retreatment remains to be better defined to guide the choice of the new regimen before retreatment in DAA-experienced patients.

Promises and pitfalls of Illumina sequencing for HIV resistance genotyping

Genetic sequencing ("genotyping") plays a critical role in the modern clinical management of HIV infection. This virus evolves rapidly within patients because of its error-prone reverse transcriptase and short generation time. Consequently, HIV variants with mutations that confer resistance to one or more antiretroviral drugs can emerge during sub-optimal treatment. There are now multiple HIV drug resistance interpretation algorithms that take the region of the HIV genome encoding the major drug targets as inputs; expert use of these algorithms can significantly improve to clinical outcomes in HIV treatment. Next-generation sequencing has the potential to revolutionize HIV resistance genotyping by lowering the threshold that rare but clinically significant HIV variants can be detected reproducibly, and by conferring improved cost-effectiveness in high-throughput scenarios. In this review, we discuss the relative merits and challenges of deploying the Illumina MiSeq instrument for clinical HIV genotyping.

MinVar: A rapid and versatile tool for HIV-1 drug resistance genotyping by deep sequencing

Genotypic monitoring of drug-resistance mutations (DRMs) in HIV-1 infected individuals is strongly recommended to guide selection of the initial antiretroviral therapy (ART) and changes of drug regimens. Traditionally, mutations conferring drug resistance are detected by population sequencing of the reverse transcribed viral RNA encoding the HIV-1 enzymes target by ART, followed by manual analysis and interpretation of Sanger sequencing traces. This process is labor intensive, relies on subjective interpretation from the operator, and offers limited sensitivity as only mutations above 20% frequency can be reliably detected. Here we present MinVar, a pipeline for the analysis of deep sequencing data, which allows reliable and automated detection of DRMs down to 5%. We evaluated MinVar with data from amplicon sequencing of defined mixtures of molecular virus clones with known DRM and plasma samples of viremic HIV-1 infected individuals and we compared it to VirVarSeq, another virus variant detection tool exclusively working on Illumina deep sequencing data. MinVar was designed to be compatible with a diverse range of sequencing platforms and allows the detection of DRMs and insertions/deletions from deep sequencing data without the need to perform additional bioinformatics analysis, a prerequisite to a widespread implementation of HIV-1 genotyping using deep sequencing in routine diagnostic settings.

Recent advances in inferring viral diversity from high-throughput sequencing data

Rapidly evolving RNA viruses prevail within a host as a collection of closely related variants, referred to as viral quasispecies. Advances in high-throughput sequencing (HTS) technologies have facilitated the assessment of the genetic diversity of such virus populations at an unprecedented level of detail. However, analysis of HTS data from virus populations is challenging due to short, error-prone reads. In order to account for uncertainties originating from these limitations, several computational and statistical methods have been developed for studying the genetic heterogeneity of virus population. Here, we review methods for the analysis of HTS reads, including approaches to local diversity estimation and global haplotype reconstruction. Challenges posed by aligning reads, as well as the impact of reference biases on diversity estimates are also discussed. In addition, we address some of the experimental approaches designed to improve the biological signal-to-noise ratio. In the future, computational methods for the analysis of heterogeneous virus populations are likely to continue being complemented by technological developments.

Quasispecies Analyses of the HIV-1 Near-full-length Genome With Illumina MiSeq

Human immunodeficiency virus type-1 (HIV-1) exhibits high between-host genetic diversity and within-host heterogeneity, recognized as quasispecies. Because HIV-1 quasispecies fluctuate in terms of multiple factors, such as antiretroviral exposure and host immunity, analyzing the HIV-1 genome is critical for selecting effective antiretroviral therapy and understanding within-host viral coevolution mechanisms. Here, to obtain HIV-1 genome sequence information that includes minority variants, we sought to develop a method for evaluating quasispecies throughout the HIV-1 near-full-length genome using the Illumina MiSeq benchtop deep sequencer. To ensure the reliability of minority mutation detection, we applied an analysis method of sequence read mapping onto a consensus sequence derived from de novo assembly followed by iterative mapping and subsequent unique error correction. Deep sequencing analyses of aHIV-1 clone showed that the analysis method reduced erroneous base prevalence below 1% in each sequence position and discarded only < 1% of all collected nucleotides, maximizing the usage of the collected genome sequences. Further, we designed primer sets to amplify the HIV-1 near-full-length genome from clinical plasma samples. Deep sequencing of 92 samples in combination with the primer sets and our analysis method provided sufficient coverage to identify >1%-frequency sequences throughout the genome. When we evaluated sequences of pol genes from 18 treatment-naïve patients' samples, the deep sequencing results were in agreement with Sanger sequencing and identified numerous additional minority mutations. The results suggest that our deep sequencing method would be suitable for identifying within-host viral population dynamics throughout the genome.

QQ-SNV: single nucleotide variant detection at low frequency by comparing the quality quantiles

Background

Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth (“deep sequencing”), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset.

Results

For default application of QQ-SNV, variants were called using a SNV probability cutoff of 0.5 (QQ-SNV_D). To improve the sensitivity we used a SNV probability cutoff of 0.0001 (QQ-SNV_HS). To also increase specificity, SNVs called were overruled when their frequency was below the 80^th percentile calculated on the distribution of error frequencies (QQ-SNV_HS-P80).

When comparing QQ-SNV versus the other methods on the plasmid mixture test sets, QQ-SNV_D performed similarly to the existing approaches. QQ-SNV_HS was more sensitive on all test sets but with more false positives. QQ-SNV_HS-P80 was found to be the most accurate method over all test sets by balancing sensitivity and specificity. When applied to a paired-end HCV sequencing study, with lowest spiked-in true frequency of 0.5 %, QQ-SNV_HS-P80 revealed a sensitivity of 100 % (vs. 40–60 % for the existing methods) and a specificity of 100 % (vs. 98.0–99.7 % for the existing methods). In addition, QQ-SNV required the least overall computation time to process the test sets.

Finally, when testing on a clinical sample, four putative true variants with frequency below 0.5 % were consistently detected by QQ-SNV_HS-P80 from different generations of Illumina sequencers.

Conclusions

We developed and successfully evaluated a novel method, called QQ-SNV, for highly efficient single nucleotide variant calling on Illumina deep sequencing virology data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0812-9) contains supplementary material, which is available to authorized users.

Association between variants in the interferon lambda 4 locus and substitutions in the hepatitis C virus non-structural protein 5A

BACKGROUND & AIMS

Single nucleotide polymorphisms within the interferon lambda 4 (IFNL4) locus are strongly associated with spontaneous clearance of hepatitis C virus (HCV) infection and early viral response to interferon therapy. Interaction between host genotype and amino acid substitutions might also influence the risk of antiviral resistance in interferon-free direct acting antiviral (DAA) therapies.

METHODS

The relationship between IFNL4 genotype and HCV substitutions was analyzed in 929 patients with chronic HCV genotype 1b infection. Ultra-deep sequencing and quasispecies reconstruction was performed on the N-terminal region of NS5A in 57 patients.

RESULTS

IFNL4 genotype was strongly associated with HCV NS5A Y93 and core protein substitutions, and the number and diversity of predicted quasispecies was marginally greater in IFNL4 TT/TT patients compared to TT/ΔG, ΔG/ΔG patients. RNA secondary structure prediction of the NS5A region suggests that variable sites are more likely to occupy unpaired, high entropy positions.

CONCLUSIONS

HCV infection is proposed to induce a more efficient antiviral response in individuals with the IFNL4 TT/TT genotype that results either in viral clearance or selection for viral adaptations. The association between IFNL4 TT/TT genotype and Y93 substitutions may impact the risk of antiviral resistance in NS5A inhibitors in DAA therapy.