Applications of Next-Generation Sequencing Technologies to Diagnostic Virology

Novel DNA sequencing techniques, referred to as “next-generation” sequencing (NGS), provide high speed and throughput that can produce an enormous volume of sequences with many possible applications in research and diagnostic settings. In this article, we provide an overview of the many applications of NGS in diagnostic virology. NGS techniques have been used for high-throughput whole viral genome sequencing, such as sequencing of new influenza viruses, for detection of viral genome variability and evolution within the host, such as investigation of human immunodeficiency virus and human hepatitis C virus quasispecies, and monitoring of low-abundance antiviral drug-resistance mutations. NGS techniques have been applied to metagenomics-based strategies for the detection of unexpected disease-associated viruses and for the discovery of novel human viruses, including cancer-related viruses. Finally, the human virome in healthy and disease conditions has been described by NGS-based metagenomics

Quasispecies dynamics and treatment outcome during early hepatitis C infection in a cohort of HIV-infected men

Hepatitis C virus (HCV) is emerging as one of the leading causes of morbidity and mortality in individuals infected with HIV and has overtaken AIDS-defining illnesses as a cause of death in HIV patient populations who have access to highly active antiretroviral therapy. For many years, the clonal analysis was the reference method for investigating viral diversity. In this thesis, a next generation sequencing (NGS) approach was developed using 454 pyrosequencing and Illumina-based technology. A sequencing pipeline was developed using two different NGS approaches, nested PCR, and metagenomics. The pipeline was used to study the viral populations in the sera of HCV-infected patients from a unique cohort of 160 HIV-positive patients with early HCV infection. These pipelines resulted in an improved understanding of HCV quasispecies dynamics, especially regarding studying response to treatment. Low viral diversity at baseline correlated with sustained virological response (SVR) while high viral diversity at baseline was associated with treatment failure. The emergence of new viral strains following treatment failure was most commonly associated with emerging dominance of pre-existing minority variants rather than re-infection. In the new era of direct-acting antivirals, next generation sequencing technologies are the most promising tool for identifying minority variants present in the HCV quasispecies populations at baseline. In this cohort, several mutations conferring resistance were detected in genotype 1a treatment-naïve patients. Further research into the impact of baseline HCV variants on SVR rates should be carried out in this population. A clearer understanding of the properties of viral quasispecies would enable clinicians to make improved treatment choices for their patients

Efficient error correction for next-generation sequencing of viral amplicons

<p>Abstract</p> <p>Background</p> <p>Next-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.</p> <p>Results</p> <p>In this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones.</p> <p>Conclusions</p> <p>Both algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.</p> <p>The implementations of the algorithms and data sets used for their testing are available at: <url>http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm</url></p

Computational Methods for Sequencing and Analysis of Heterogeneous RNA Populations

Next-generation sequencing (NGS) and mass spectrometry technologies bring unprecedented throughput, scalability and speed, facilitating the studies of biological systems. These technologies allow to sequence and analyze heterogeneous RNA populations rather than single sequences. In particular, they provide the opportunity to implement massive viral surveillance and transcriptome quantification. However, in order to fully exploit the capabilities of NGS technology we need to develop computational methods able to analyze billions of reads for assembly and characterization of sampled RNA populations. In this work we present novel computational methods for cost- and time-effective analysis of sequencing data from viral and RNA samples. In particular, we describe: i) computational methods for transcriptome reconstruction and quantification; ii) method for mass spectrometry data analysis; iii) combinatorial pooling method; iv) computational methods for analysis of intra-host viral populations

Sequential Bottlenecks Drive Viral Evolution in Early Acute Hepatitis C Virus Infection

Hepatitis C is a pandemic human RNA virus, which commonly causes chronic infection and liver disease. The characterization of viral populations that successfully initiate infection, and also those that drive progression to chronicity is instrumental for understanding pathogenesis and vaccine design. A comprehensive and longitudinal analysis of the viral population was conducted in four subjects followed from very early acute infection to resolution of disease outcome. By means of next generation sequencing (NGS) and standard cloning/Sanger sequencing, genetic diversity and viral variants were quantified over the course of the infection at frequencies as low as 0.1%. Phylogenetic analysis of reassembled viral variants revealed acute infection was dominated by two sequential bottleneck events, irrespective of subsequent chronicity or clearance. The first bottleneck was associated with transmission, with one to two viral variants successfully establishing infection. The second occurred approximately 100 days post-infection, and was characterized by a decline in viral diversity. In the two subjects who developed chronic infection, this second bottleneck was followed by the emergence of a new viral population, which evolved from the founder variants via a selective sweep with fixation in a small number of mutated sites. The diversity at sites with non-synonymous mutation was higher in predicted cytotoxic T cell epitopes, suggesting immune-driven evolution. These results provide the first detailed analysis of early within-host evolution of HCV, indicating strong selective forces limit viral evolution in the acute phase of infection