Accurate single nucleotide variant detection in viral populations by combining probabilistic clustering with a statistical test of strand bias

Parallel evolution and enhanced virulence upon in vivo passage of an RNA virus in Drosophila melanogaster

Virus evolution is strongly affected by antagonistic co-evolution of virus and host. Host immunity positively selects for viruses that evade the immune response, which in turn may drive counter-adaptations in host immune genes. We investigated how host immune pressure shapes virus populations, using the fruit fly Drosophila melanogaster and its natural pathogen Drosophila C virus (DCV), as a model. We performed an experimental evolution study in which DCV was serially passaged for ten generations in three fly genotypes differing in their antiviral RNAi response: wild-type flies and flies in which the endonuclease gene Dicer-2 was either overexpressed or inactivated. All evolved virus populations replicated more efficiently in vivo and were more virulent than the parental stock. The number of polymorphisms increased in all three host genotypes with passage number, which was most pronounced in Dicer-2 knockout flies. Mutational analysis showed strong parallel evolution, as mutations accumulated in a specific region of the VP3 capsid protein in every lineage in a host genotype-independent manner. The parental tyrosine at position ninety-five of VP3 was substituted with either one of five different amino acids in fourteen out of fifteen lineages. However, no consistent amino acid changes were observed in the viral RNAi suppressor gene 1A, nor elsewhere in the genome in any of the host backgrounds. Our study indicates that the RNAi response restricts the sequence space that can be explored by viral populations. Moreover, our study illustrates how evolution towards higher virulence can be a highly reproducible, yet unpredictable process.

Doomed drones? Using passage experiments and mathematical modelling to determine Deformed wing virus population dynamics in male honeybees

Varroa destructor is an ectoparasitic mite of honeybees which vectors a range of pathogenic viruses, the most notable being Deformed wing virus (DWV). Mites parasitise bees during pupal development and male honeybees, drones, have a longer development cycle than female workers (24 versus 21 days), allow for more progeny mites to develop per foundress (1.6-2.5 compared to 0.7-1.45). How this longer exposure time influences evolution of the transmitted virus population is unknown. Using uniquely tagged viruses recovered from cDNA we investigated the replication, competition and morbidity of DWV genotypes in drones. Assays examining virus replication and morbidity revealed drones are highly susceptible to both predominant genotypes of DWV. In virus passage studies using an equimolar inocula of major DNA genotypes and their recombinants, the recombinant form dominated but did not reach 100% of the virus population within 10 passages. Using an in-silico model of the virus-mite-bee system we examined bottlenecks during virus acquisition by the mite and subsequent injection of viruses into the host, which may play a significant role in shaping virus diversity. This study furthers our understanding of the variables influencing DWV diversity changes and provides insight into areas of future research in the mite-virus-bee system.

Combined direct/indirect detection allows identification of DNA termini in diverse sequencing datasets and supports a multiple-initiation-site model for HIV plus-strand synthesis

Replication of genetic material involves the creation of characteristic termini. Determining these termini is important to refine our understanding of the mechanisms involved in maintaining the genomes of cellular organisms and viruses. Here we describe a computational approach combining direct and indirect readouts to detect termini from next-generation short-read sequencing. While a direct inference of termini can come from mapping the most prominent start positions of captured DNA fragments, this approach is insufficient in cases where the DNA termini are not captured, whether for biological or technical reasons. Thus, a complementary (indirect) approach to terminus detection can be applied, taking advantage of the imbalance in coverage between forward and reverse sequence reads near termini. A resulting metric ("strand bias") can be used to detect termini even where termini are naturally blocked from capture or ends are not captured during library preparation (e.g., in tagmentation-based protocols). Applying this analysis to datasets where known DNA termini are present, such as from linear double-stranded viral genomes, yielded distinct strand bias signals corresponding to these termini. To evaluate the potential to analyze a more complex situation, we applied the analysis to examine DNA termini present early after HIV infection in a cell culture model. We observed both the known termini expected based on standard models of HIV reverse transcription (the U5-right-end and U3-left-end termini) as well as a signal corresponding to a previously described additional initiation site for plus-strand synthesis (cPPT [central polypurine tract]). Interestingly, we also detected putative terminus signals at additional sites. The strongest of these are a set that share several characteristics with the previously characterized plus-strand initiation sites (the cPPT and 3' PPT [polypurine tract] sites): (i) an observed spike in directly captured cDNA ends, an indirect terminus signal evident in localized strand bias, (iii) a preference for location on the plus-strand, (iv) an upstream purine-rich motif, and (v) a decrease in terminus signal at late time points after infection. These characteristics are consistent in duplicate samples in two different genotypes (wild type and integrase-lacking HIV). The observation of distinct internal termini associated with multiple purine-rich regions raises a possibility that multiple internal initiations of plus-strand synthesis might contribute to HIV replication.

Genetic characterization of a novel pheasant-origin orthoreovirus using Next-Generation Sequencing

A field isolate (Reo/SDWF /Pheasant/17608/20) of avian orthoreovirus (ARV), isolated from a flock of game-pheasants in Weifang, Shandong Province, was genetically characterized being a field variant or novel strain in our recent research studies in conducting whole genome sequencing by using Next-Generation Sequencing (NGS) technique on Illumina MiSeq platform. Among a total of 870,197 35-151-mer sequencing reads, 297,711 reads (34.21%) were identified as ARV sequences. The de novo assembly of the ARV reads resulted in generation of 10 ARV-related contigs with the average sequencing coverage from 1390× to 1977× according to 10 ARV genome segments. The complete genomes of this pheasant-origin ARV (Reo/SDWF /Pheasant/17608/20) were 23,495 bp in length and consist of 10 dsRNA segments ranged from 1192 bp (S4) to 3958 bp (L1) encoding 12 viral proteins. Sequence comparison between the SDWF17608 and classic ARV reference strains revealed that 58.1-100% nucleotide (nt) identities and 51.4-100% amino acid (aa) identities were in genome segment coding genes. The 10 RNA segments had conversed termini at 5' (5'-GCUUUU) and 3' (UCAUC-3') side, which were identical to the most published ARV strains. Phylogenetic analysis revealed that this pheasant ARV field variant was closely related with chicken ARV strains in 7 genome segment genes, but it possessed significant sequence divergence in M1, M3 and S2 segments. These findings suggested that this pheasant-origin field variant was a divergent ARV strain and was likely originated from reassortments between different chicken ARV strains.

Quantitative and Qualitative Changes in the Deformed Wing Virus Population in Honey Bees Associated with the Introduction or Removal of Varroa destructor

Varroa destructor is an ectoparasitic mite associated with significant losses of honeybee colonies globally. The mite vectors a range of pathogenic viruses, the most important of which is the Deformed wing virus (DWV). In the absence of Varroa, DWV exists as a low-level, highly diverse virus population. However, when transmitted by Varroa, certain variants become highly elevated, and may become near-clonal and cause symptomatic infections. Mite transmission between colonies can occur when parasitised workers drift from or rob adjacent hives. These activities can result in elevated mite levels, but the resulting change in the DWV population, the primary determinant of winter colony losses, has not been determined. In reciprocal studies, we investigated the influence of the removal of mites, or their acquisition, on the DWV population. When mites were removed from heavily infested colonies, there was a striking and rapid reduction in virus load. Conversely, siting Varroa-naïve colonies in a mite-infested apiary resulted in the acquisition of mites and concomitant changes in the virus population. We observed both near-clonal and highly divergent virus populations regardless of titre, suggesting changes were stochastic and colony-specific. Our findings have implications for the outcome of strategies in areas with total or patchy implementation of Varroa control plans.

Genomic evolution of the marine bacterium Phaeobacter inhibens during biofilm growth

P. inhibens 2.10 is an effective biofilm former on marine surfaces and has the ability to outcompete other microorganisms, possibly due to the production of the plasmid-encoded, secondary metabolite tropodithietic acid (TDA). P. inhibens 2.10 biofilms produce phenotypic variants with reduced competitiveness compared to the wild-type. In the present study, we used longitudinal, genome-wide deep sequencing to uncover the genetic foundation that contributes to the emergent phenotypic diversity in P. inhibens 2.10 biofilm dispersants. Our results show that phenotypic variation is not due to the loss of plasmid that encodes the genes for the TDA synthesis, but instead show that P. inhibens 2.10 biofilm populations become rapidly enriched in single nucleotide variations in genes involved in the synthesis of TDA. While variants in genes previously linked to other phenotypes, such as lipopolysaccharide production (i.e. rfbA) and celluar persistence (i.e. metG), also appear to be selected for during biofilm dispersal, the number and consistency of variations found for genes involved in TDA production suggest that this metabolite imposes a burden for P. inhibens 2.10 cells. Our results indicate a strong selection pressure for the loss of TDA in mono-species biofilm populations and provide insight into how competition (or lack thereof) in biofilms might shape genome evolution in bacteria. Importance Statement Biofilm formation and dispersal are important survival strategies for environmental bacteria. During biofilm dispersal cells often display stable and heritable variants from the parental biofilm. Phaeobacter inhibens is an effective colonizer of marine surfaces, in which a subpopulation of its biofilm dispersal cells displays a non-competitive phenotype. This study aimed to elucidate the genetic basis of these phenotypic changes. Despite the progress made to date in characterizing the dispersal variants in P. inhibens, little is understood about the underlying genetic changes that result in the development of the specific variants. Here, P. inhibens phenotypic variation was linked to single nucleotide polymorphisms (SNPs), in particular in genes affecting the competitive ability of P. inhibens, including genes related to the production of the antibiotic tropodithietic acid (TDA) and bacterial cell-cell communication (e.g. quorum sensing). This work is significant as it reveals how the biofilm-lifestyle might shape genome evolution in a cosmopolitan bacterium.

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

Motivation

High-throughput sequencing technologies are used increasingly not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations.

Results

To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape.

Availabilityand implementation

V-pipe is freely available at https://github.com/cbg-ethz/V-pipe.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evidence for and against deformed wing virus spillover from honey bees to bumble bees: a reverse genetic analysis

Deformed wing virus (DWV) is a persistent pathogen of European honey bees and the major contributor to overwintering colony losses. The prevalence of DWV in honey bees has led to significant concerns about spillover of the virus to other pollinating species. Bumble bees are both a major group of wild and commercially-reared pollinators. Several studies have reported pathogen spillover of DWV from honey bees to bumble bees, but evidence of a sustained viral infection characterized by virus replication and accumulation has yet to be demonstrated. Here we investigate the infectivity and transmission of DWV in bumble bees using the buff-tailed bumble bee Bombus terrestris as a model. We apply a reverse genetics approach combined with controlled laboratory conditions to detect and monitor DWV infection. A novel reverse genetics system for three representative DWV variants, including the two master variants of DWV—type A and B—was used. Our results directly confirm DWV replication in bumble bees but also demonstrate striking resistance to infection by certain transmission routes. Bumble bees may support DWV replication but it is not clear how infection could occur under natural environmental conditions.

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.

Association of CMV genomic mutations with symptomatic infection and hearing loss in congenital CMV infection

Background

Congenital cytomegalovirus (cCMV) infection is the most common congenital infection and a leading cause of long-term neurological and sensory sequelae, the most common being sensorineural hearing loss (SNHL). Despite extensive research, clinical or laboratory markers to identify CMV infected children with increased risk for disease have not been identified. This study utilizes viral whole-genome next generation-sequencing (NGS) of specimens from congenitally infected infants to explore viral diversity and specific viral variants that may be associated with symptomatic infection and SNHL.

Methods

CMV DNA from urine specimens of 30 infants (17 asymptomatic, 13 symptomatic) was target enriched and next generation sequenced resulting in 93% coverage of the CMV genome allowing analysis of viral diversity.

Results

Variant frequency distribution was compared between children with symptomatic and asymptomatic cCMV and those with (n = 13) and without (n = 17) hearing loss. The CMV genes UL48A, UL88, US19 and US22 were found to have an increase in nucleotide diversity in symptomatic children; while UL57, UL20, UL104, US14, UL115, and UL35 had an increase in diversity in children with hearing loss. An analysis of single variant differences between symptomatic and asymptomatic children found UL55 to have the highest number, while the most variants associated with SNHL were in the RL11 gene family. In asymptomatic infants with SNHL, mutations were observed more frequently in UL33 and UL20.

Conclusion

CMV genomes from infected newborns can be mapped to 93% of the genome at a depth allowing accurate and reproducible analysis of polymorphisms for variant and gene discovery that may be linked to symptomatic and hearing loss outcomes.

Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries

Synthetic biology utilizes the Design-Build-Test-Learn pipeline for the engineering of biological systems. Typically, this requires the construction of specifically designed, large and complex DNA assemblies. The availability of cheap DNA synthesis and automation enables high-throughput assembly approaches, which generates a heavy demand for DNA sequencing to verify correctly assembled constructs. Next-generation sequencing is ideally positioned to perform this task, however with expensive hardware costs and bespoke data analysis requirements few laboratories utilize this technology in-house. Here a workflow for highly multiplexed sequencing is presented, capable of fast and accurate sequence verification of DNA assemblies using nanopore technology. A novel sample barcoding system using polymerase chain reaction is introduced, and sequencing data are analyzed through a bespoke analysis algorithm. Crucially, this algorithm overcomes the problem of high-error rate nanopore data (which typically prevents identification of single nucleotide variants) through statistical analysis of strand bias, permitting accurate sequence analysis with single-base resolution. As an example, 576 constructs (6 × 96 well plates) were processed in a single workflow in 72 h (from Escherichia coli colonies to analyzed data). Given our procedure's low hardware costs and highly multiplexed capability, this provides cost-effective access to powerful DNA sequencing for any laboratory, with applications beyond synthetic biology including directed evolution, single nucleotide polymorphism analysis and gene synthesis.

Investigation of somatic single nucleotide variations in human endogenous retrovirus elements and their potential association with cancer

Human endogenous retroviruses (HERVs) have been investigated for potential links with human cancer. However, the distribution of somatic nucleotide variations in HERV elements has not been explored in detail. This study aims to identify HERV elements with an over-representation of somatic mutations (hot spots) in cancer patients. Four HERV elements with mutation hotspots were identified that overlap with exons of four human protein coding genes. These hotspots were identified based on the significant over-representation (p<8.62e-4) of non-synonymous single-nucleotide variations (nsSNVs). These genes are TNN (HERV-9/LTR12), OR4K15 (HERV-IP10F/LTR10F), ZNF99 (HERV-W/HERV17/LTR17), and KIR2DL1 (MST/MaLR). In an effort to identify mutations that effect survival, all nsSNVs were further evaluated and it was found that kidney cancer patients with mutation C2270G in ZNF99 have a significantly lower survival rate (hazard ratio = 2.6) compared to those without it. Among HERV elements in the human non-protein coding regions, we found 788 HERVs with significantly elevated numbers of somatic single-nucleotide variations (SNVs) (p<1.60e-5). From this category the top three HERV elements with significantly over-represented SNVs are HERV-H/LTR7, HERV-9/LTR12 and HERV-L/MLT2. Majority of the SNVs in these 788 HERV elements are located in three DNA functional groups: long non-coding RNAs (lncRNAs) (60%), introns (22.2%) and transcriptional factor binding sites (TFBS) (14.8%). This study provides a list of mutational hotspots in HERVs, which could potentially be used as biomarkers and therapeutic targets.

Exposure to Arsenic Alters the Microbiome of Larval Zebrafish

Exposure to environmental toxins such as heavy metals can perturb the development and stability of microbial communities associated with human or animal hosts. Widespread arsenic contamination in rivers and riparian habitats therefore presents environmental and health concerns for populations living near sources of contamination. To investigate how arsenic affects host microbiomes, we sequenced and characterized the microbiomes of twenty larval zebrafish exposed to three concentrations of arsenic that are found in contaminated water—low (10 ppb), medium (50 ppb), and high (100 ppb) for 20 days. We found that even a small concentration of arsenic changed the overall microbial composition, structure and diversity of microbial communities, causing dysbiosis in developing larval zebrafish microbiota. In addition, we found that a high concentration of arsenic also increased the abundance of a class 1 integron, an integrase-dependent system facilitating the horizontal transfer of genes conferring resistance to heavy metals and antibiotics.

Isolation and genomic characterization of gosling gout caused by a novel goose astrovirus

A severe infectious disease characterized with gout, haemorrhage and swellings of kidneys has affected goslings around the major goose-producing regions in China since November 2016. A Novel goose-origin astrovirus (AStV), designated as AStV/SDPY/Goose/1116/17 (AStV-SDPY) strain, was isolated from diseased goslings, and experimental reproduction of gout was successful using the AStV-SDPY strain. Additionally, the AStV-SDPY was conducted for its full genome sequencing characterization using next-generation sequencing (NGS) technique on Illumina HiSeq platform. A complete genome of the AStV-SDPY was 7,252 nt in length and encoded three viral proteins. Phylogenetic analysis revealed that AStV-SDPY strain belongs to an independent branch of avian astroviruses, and the nucleotide homology among AStV-SDPY and other classic avian astrovirus strains was only 48.8%-68.2%. Results of above data indicated the causative agent of the gosling gout occurring in China is a novel divergent goose astrovirus.

A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data

Detection of somatic mutations holds great potential in cancer treatment and has been a very active research field in the past few years, especially since the breakthrough of the next-generation sequencing technology. A collection of variant calling pipelines have been developed with different underlying models, filters, input data requirements, and targeted applications. This review aims to enumerate these unique features of the state-of-the-art variant callers, in the hope to provide a practical guide for selecting the appropriate pipeline for specific applications. We will focus on the detection of somatic single nucleotide variants, ranging from traditional variant callers based on whole genome or exome sequencing of paired tumor-normal samples to recent low-frequency variant callers designed for targeted sequencing protocols with unique molecular identifiers. The variant callers have been extensively benchmarked with inconsistent performances across these studies. We will review the reference materials, datasets, and performance metrics that have been used in the benchmarking studies. In the end, we will discuss emerging trends and future directions of the variant calling algorithms.

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.

DADA2: High-resolution sample inference from Illumina amplicon data

We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

Effects of long-term tenofovir-based combination antiretroviral therapy in HIV-hepatitis B virus coinfection on persistent hepatitis B virus viremia and the role of hepatitis B virus quasispecies diversity

OBJECTIVE

Hepatitis B virus (HBV) can persist in some HIV-HBV coinfected individuals on tenofovir disoproxil fumarate (TDF)-containing combination antiretroviral therapy (cART) but HBV resistance to TDF has not been reported and the source of persistent HBV DNA on TDF is poorly understood. The aims of this study were to assess long-term HBV suppression in HIV-HBV coinfected individuals receiving TDF and investigate quasispecies variation using ultradeep pyrosequencing (UDPS).

METHODS

Ninety-two HIV-HBV coinfected participants on, or about to commence, TDF-containing cART were enrolled [Australia (n = 40), Thailand (n = 52)] and followed for 2 years with study visits every 6 months. HBV reverse transcriptase sequencing was performed on samples with HBV DNA more than 400 IU/ml by population-based methods and UDPS. Quasispecies diversity was assessed using Shannon entropy.

RESULTS

Over 24 months, viremia was detected at least once in 17% (n = 16) of the cohort. Novel mutations were not identified in on TDF samples tested by population-based sequencing (n = 19). Using UDPS, the median Shannon entropy value in samples prior to TDF in patients aviremic on TDF was not statistically different from those who were viremic on TDF (n = 50; 8.4 and 9.1, respectively, P = 0.9). Longitudinal Shannon entropy analysis of on TDF samples from five participants showed three individuals with significant changes in viral diversity over time.

CONCLUSION

Persistent viremia on TDF-containing cART is common but TDF-resistance was not detected. In some individuals, changes in viral diversity over time were observed on TDF which could potentially be active viral replication. Further follow-up will be needed to determine the clinical significance of detectable HBV DNA on TDF-containing cART.

A Comprehensive Analysis of Primer IDs to Study Heterogeneous HIV-1 Populations

Determining the composition of viral populations is becoming increasingly important in the field of medical virology. While recently developed computational tools for viral haplotype analysis allow for correcting sequencing errors, they do not always allow for the removal of errors occurring in the upstream experimental protocol, such as PCR errors. Primer IDs (pIDs) are one method to address this problem by harnessing redundant template resampling for error correction. By using a reference mixture of five HIV-1 strains, we show how pIDs can be useful for estimating key experimental parameters, such as the substitution rate of the PCR process and the reverse transcription (RT) error rate. In addition, we introduce a hidden Markov model for determining the recombination rate of the RT PCR process. We found no strong sequence-specific bias in pID abundances (the same RT efficiencies as compared to commonly used short, specific RT primers) and no effects of pIDs on the estimated distribution of the references viruses.

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.

Amplicon Resequencing Identified Parental Mosaicism for Approximately 10% of "de novo" SCN1A Mutations in Children with Dravet Syndrome

The majority of children with Dravet syndrome (DS) are caused by de novo SCN1A mutations. To investigate the origin of the mutations, we developed and applied a new method that combined deep amplicon resequencing with a Bayesian model to detect and quantify allelic fractions with improved sensitivity. Of 174 SCN1A mutations in DS probands which were considered "de novo" by Sanger sequencing, we identified 15 cases (8.6%) of parental mosaicism. We identified another five cases of parental mosaicism that were also detectable by Sanger sequencing. Fraction of mutant alleles in the 20 cases of parental mosaicism ranged from 1.1% to 32.6%. Thirteen (65% of 20) mutations originated paternally and seven (35% of 20) maternally. Twelve (60% of 20) mosaic parents did not have any epileptic symptoms. Their mutant allelic fractions were significantly lower than those in mosaic parents with epileptic symptoms (P = 0.016). We identified mosaicism with varied allelic fractions in blood, saliva, urine, hair follicle, oral epithelium, and semen, demonstrating that postzygotic mutations could affect multiple somatic cells as well as germ cells. Our results suggest that more sensitive tools for detecting low-level mosaicism in parents of families with seemingly "de novo" mutations will allow for better informed genetic counseling.

Detection of Low-Level Mixed-Population Drug Resistance in Mycobacterium tuberculosis Using High Fidelity Amplicon Sequencing

Undetected and untreated, low-levels of drug resistant (DR) subpopulations in clinical Mycobacterium tuberculosis (Mtb) infections may lead to development of DR-tuberculosis, potentially resulting in treatment failure. Current phenotypic DR susceptibility testing has a theoretical potential for 1% sensitivity, is not quantitative, and requires several weeks to complete. The use of "single molecule-overlapping reads" (SMOR) analysis with next generation DNA sequencing for determination of ultra-rare target alleles in complex mixtures provides increased sensitivity over standard DNA sequencing. Ligation free amplicon sequencing with SMOR analysis enables the detection of resistant allele subpopulations at ≥0.1% of the total Mtb population in near real-time analysis. We describe the method using standardized mixtures of DNA from resistant and susceptible Mtb isolates and the assay's performance for detecting ultra-rare DR subpopulations in DNA extracted directly from clinical sputum samples. SMOR analysis enables rapid near real-time detection and tracking of previously undetectable DR sub-populations in clinical samples allowing for the evaluation of the clinical relevance of low-level DR subpopulations. This will provide insights into interventions aimed at suppressing minor DR subpopulations before they become clinically significant.

Massively parallel sequencing fails to detect minor resistant subclones in tissue samples prior to tyrosine kinase inhibitor therapy

BACKGROUND

Personalised medicine and targeted therapy have revolutionised cancer treatment. However, most patients develop drug resistance and relapse after showing an initial treatment response. Two theories have been postulated; either secondary resistance mutations develop de novo during therapy by mutagenesis or they are present in minor subclones prior to therapy. In this study, these two theories were evaluated in gastrointestinal stromal tumours (GISTs) where most patients develop secondary resistance mutations in the KIT gene during therapy with tyrosine kinase inhibitors.

METHODS

We used a cohort of 33 formalin-fixed, paraffin embedded (FFPE) primary GISTs and their corresponding recurrent tumours with known mutational status. The primary tumours were analysed for the secondary mutations of the recurrences, which had been identified previously. The primary tumours were resected prior to tyrosine kinase inhibitor therapy. Three ultrasensitive, massively parallel sequencing approaches on the GS Junior (Roche, Mannheim, Germany) and the MiSeq(TM) (Illumina, San Diego, CA, USA) were applied. Additionally, nine fresh-frozen samples resected prior to therapy were analysed for the most common secondary resistance mutations.

RESULTS

With a sensitivity level of down to 0.02%, no pre-existing resistant subclones with secondary KIT mutations were detected in primary GISTs. The sensitivity level varied for individual secondary mutations and was limited by sequencing artefacts on both systems. Artificial T > C substitutions at the position of the exon 13 p.V654A mutation, in particular, led to a lower sensitivity, independent from the source of the material. Fresh-frozen samples showed the same range of artificially mutated allele frequencies as the FFPE material.

CONCLUSIONS

Although we achieved a sufficiently high level of sensitivity, neither in the primary FFPE nor in the fresh-frozen GISTs we were able to detect pre-existing resistant subclones of the corresponding known secondary resistance mutations of the recurrent tumours. This supports the theory that secondary KIT resistance mutations develop under treatment by "de novo" mutagenesis. Alternatively, the detection limit of two mutated clones in 10,000 wild-type clones might not have been high enough or heterogeneous tissue samples, per se, might not be suitable for the detection of very small subpopulations of mutated cells.

Transmitted/Founder Viruses Rapidly Escape from CD8+ T Cell Responses in Acute Hepatitis C Virus Infection

The interaction between hepatitis C virus (HCV) and cellular immune responses during very early infection is critical for disease outcome. To date, the impact of antigen-specific cellular immune responses on the evolution of the viral population establishing infection and on potential escape has not been studied. Understanding these early host-virus dynamics is important for the development of a preventative vaccine. Three subjects who were followed longitudinally from the detection of viremia preseroconversion until disease outcome were analyzed. The evolution of transmitted/founder (T/F) viruses was undertaken using deep sequencing. CD8(+) T cell responses were measured via enzyme-linked immunosorbent spot (ELISpot) assay using HLA class I-restricted T/F epitopes. T/F viruses were rapidly extinguished in all subjects associated with either viral clearance (n = 1) or replacement with viral variants leading to establishment of chronic infection (n = 2). CD8(+) T cell responses against 11 T/F epitopes were detectable by 33 to 44 days postinfection, and 5 of these epitopes had not previously been reported. These responses declined rapidly in those who became chronically infected and were maintained in the subject who cleared infection. Higher-magnitude CD8(+) T cell responses were associated with rapid development of immune escape variants at a rate of up to 0.1 per day. Rapid escape from CD8(+) T cell responses has been quantified for the first time in the early phase of primary HCV infection. These rapid escape dynamics were associated with higher-magnitude CD8(+) T cell responses. These findings raise questions regarding optimal selection of immunogens for HCV vaccine development and suggest that detailed analysis of individual epitopes may be required.

IMPORTANCE

A major limitation in our detailed understanding of the role of immune response in HCV clearance has been the lack of data on very early primary infection when the transmitted viral variants successfully establish the acute infection. This study was made possible through the availability of specimens from a unique cohort of asymptomatic primary infection cases in whom the first available viremic samples were collected approximately 3 weeks postinfection and at regular intervals thereafter. The study included detailed examination of both the evolution of the viral population and the host cellular immune responses against the T/F viruses. The findings here provide the first evidence of host cellular responses targeting T/F variants and imposing a strong selective force toward viral escape. The results of this study provide useful insight on how virus escapes the host response and consequently on future analysis of vaccine-induced immunity.

Deep sequencing analysis of viral infection and evolution allows rapid and detailed characterization of viral mutant spectrum

Motivation: The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations.

Results: Applying ViVan on deep sequenced data obtained from samples that were previously characterized by more classical approaches, we uncovered novel and potentially crucial aspects of virus populations. With our experimental work, we illustrate how ViVan can be used for studies ranging from the more practical, detection of resistant mutations and effects of antiviral treatments, to the more theoretical temporal characterization of the population in evolutionary studies.

Availability and implementation: Freely available on the web at http://www.vivanbioinfo.org

Contact: nshomron@post.tau.ac.il

Supplementary information:Supplementary data are available at Bioinformatics online.

VERSE: a novel approach to detect virus integration in host genomes through reference genome customization

Fueled by widespread applications of high-throughput next generation sequencing (NGS) technologies and urgent need to counter threats of pathogenic viruses, large-scale studies were conducted recently to investigate virus integration in host genomes (for example, human tumor genomes) that may cause carcinogenesis or other diseases. A limiting factor in these studies, however, is rapid virus evolution and resulting polymorphisms, which prevent reads from aligning readily to commonly used virus reference genomes, and, accordingly, make virus integration sites difficult to detect. Another confounding factor is host genomic instability as a result of virus insertions. To tackle these challenges and improve our capability to identify cryptic virus-host fusions, we present a new approach that detects Virus intEgration sites through iterative Reference SEquence customization (VERSE). To the best of our knowledge, VERSE is the first approach to improve detection through customizing reference genomes. Using 19 human tumors and cancer cell lines as test data, we demonstrated that VERSE substantially enhanced the sensitivity of virus integration site detection. VERSE is implemented in the open source package VirusFinder 2 that is available at http://bioinfo.mc.vanderbilt.edu/VirusFinder/.

Short duration of lamivudine for the prevention of hepatitis B virus transmission in pregnancy: lack of potency and selection of resistance mutations

This study sought to assess the antiviral efficacy of lamivudine (LMV) administered during third trimester to reduce maternal viraemia and to identify the emergence of LMV resistance. A prospective observational analysis was performed on 26 mothers with high viral load (>10⁷ IU/mL). Twenty-one women received LMV (treated group) for an average of 53 days (range 22-88 days), and the remaining five formed the untreated control group. Serum samples from two time points were used to measure HBV DNA levels and antiviral drug resistance. The LMV-treated women achieved a median HBV DNA reduction of 2.6-log10 IU/mL. Although end-of-treatment (EOT) HBV DNA in four (18%) LMV-treated women remained at >10(7) IU/mL (± 0.5 log IU/mL), no mother-to-baby transmission was observed. In contrast, a baby from the untreated mother was HBsAg positive at 9 months postpartum. Four technologies were used for drug resistance testing. Only ultra-deep pyrosequencing (UDPS) was sufficiently sensitive to detect minor viral variants down to <1%. UDPS showed that LMV therapy resulted in increased viral quasispecies diversity and positive selection of HBV variants with reverse transcriptase amino acid substitutions at sites associated with primary LMV resistance (rtM204I/V and rtA181T) in four (19%) women. These viral variants were detected mostly at low frequencies (0.63-5.92%) at EOT, but one LMV-treated mother had an rtA181T variant that increased from 2.2% pretherapy to 25.59% at EOT. This mother was also infected with the vaccine escape variant (sG145R), which was inhibited by LMV treatment. LMV therapy during late pregnancy only reduced maternal viraemia moderately, and drug-resistant viral variants emerged.

A bioinformatics pipeline for the analyses of viral escape dynamics and host immune responses during an infection

Rapidly mutating viruses, such as hepatitis C virus (HCV) and HIV, have adopted evolutionary strategies that allow escape from the host immune response via genomic mutations. Recent advances in high-throughput sequencing are reshaping the field of immuno-virology of viral infections, as these allow fast and cheap generation of genomic data. However, due to the large volumes of data generated, a thorough understanding of the biological and immunological significance of such information is often difficult. This paper proposes a pipeline that allows visualization and statistical analysis of viral mutations that are associated with immune escape. Taking next generation sequencing data from longitudinal analysis of HCV viral genomes during a single HCV infection, along with antigen specific T-cell responses detected from the same subject, we demonstrate the applicability of these tools in the context of primary HCV infection. We provide a statistical and visual explanation of the relationship between cooccurring mutations on the viral genome and the parallel adaptive immune response against HCV.

Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions

Deep sequencing harnesses the high throughput nature of next generation sequencing technologies to generate population samples, treating information contained in individual reads as meaningful. Here, we review applications of deep sequencing to pathogen evolution. Pioneering deep sequencing studies from the virology literature are discussed, such as whole genome Roche-454 sequencing analyses of the dynamics of the rapidly mutating pathogens hepatitis C virus and HIV. Extension of the deep sequencing approach to bacterial populations is then discussed, including the impacts of emerging sequencing technologies. While it is clear that deep sequencing has unprecedented potential for assessing the genetic structure and evolutionary history of pathogen populations, bioinformatic challenges remain. We summarise current approaches to overcoming these challenges, in particular methods for detecting low frequency variants in the context of sequencing error and reconstructing individual haplotypes from short reads.

Probabilistic model based error correction in a set of various mutant sequences analyzed by next-generation sequencing

To analyze the evolutionary dynamics of a mutant population in an evolutionary experiment, it is necessary to sequence a vast number of mutants by high-throughput (next-generation) sequencing technologies, which enable rapid and parallel analysis of multikilobase sequences. However, the observed sequences include many errors of base call. Therefore, if next-generation sequencing is applied to analysis of a heterogeneous population of various mutant sequences, it is necessary to discriminate between true bases as point mutations and errors of base call in the observed sequences, and to subject the sequences to error-correction processes. To address this issue, we have developed a novel method of error correction based on the Potts model and a maximum a posteriori probability (MAP) estimate of its parameters corresponding to the "true sequences". Our method of error correction utilizes (1) the "quality scores" which are assigned to individual bases in the observed sequences and (2) the neighborhood relationship among the observed sequences mapped in sequence space. The computer experiments of error correction of artificially generated sequences supported the effectiveness of our method, showing that 50-90% of errors were removed. Interestingly, this method is analogous to a probabilistic model based method of image restoration developed in the field of information engineering.