ABayesQR: A Bayesian Method for Reconstruction of Viral Populations Characterized by Low Diversity.

RNA viruses replicate with high mutation rates, creating closely related viral populations. The heterogeneous virus populations, referred to as viral quasispecies, rapidly adapt to environmental changes thus adversely affecting efficiency of antiviral drugs and vaccines. Therefore, studying the underlying genetic heterogeneity of viral populations plays a significant role in the development of effective therapeutic treatments. Recent high-throughput sequencing technologies have provided invaluable opportunity for uncovering the structure of quasispecies populations. However, accurate reconstruction of viral quasispecies remains difficult due to limited read-lengths and presence of sequencing errors. The problem is particularly challenging when the strains in a population are highly similar, i.e., the sequences are characterized by low mutual genetic distances, and further exacerbated if some of those strains are relatively rare; this is the setting where state-of-the-art methods struggle. In this paper, we present a novel viral quasispecies reconstruction algorithm, aBayesQR, that employs a maximum-likelihood framework to infer individual sequences in a mixture from high-throughput sequencing data. The search for the most likely quasispecies is conducted on long contigs that our method constructs from the set of short reads via agglomerative hierarchical clustering; operating on contigs rather than short reads enables identification of close strains in a population and provides computational tractability of the Bayesian method. Results on both simulated and real HIV-1 data demonstrate that the proposed algorithm generally outperforms state-of-the-art methods; aBayesQR particularly stands out when reconstructing a set of closely related viral strains (e.g., quasispecies characterized by low diversity).

The meeting on ‘Quasispecies: past, present and future’ took place between 17 and 18 November 2008, in Barcelona, Spain, and was organized by J. Gomez, C. Lopez‐Galindez, M.A. Martinez & A. Mas. ![][1] A meeting was held in Barcelona, Spain, in November 2008 to celebrate the 30th anniversary of the publication of the article that described the extensive genetic heterogeneity of bacteriophage Qβ (Domingo et al , 1978), which is considered to mark the beginning of experimental studies on viral quasispecies. This meeting was held at the impressive fifteenth century building of the ancient Hospital de la Santa Creu in the old town of Barcelona, which is now the headquarters of the Institut d'Estudis Catalans, and was attended by C. Weissmann (Jupiter, FL, USA), M. Billeter (Zurich, Switzerland) and E. Domingo (Madrid, Spain), who were three early protagonists of the phage Qβ work at the University of Zurich in the 1970s. Several speakers presented their results on the theoretical aspects of the population dynamics of cells and viruses, the clinical implications of quasispecies, and extensions of the quasispecies concept to cellular genes and prions. The meeting was introduced by A. Mas (Albacete, Spain), who reflected on the increasing impact that quasispecies have had in the scientific literature over the past three decades, and quoted some of the key references on viral quasispecies (Martell et al , 1992; Meyerhans et al , 1989; Najera et al , 1995; Vignuzzi et al , 2006; for a historical review of the impact of quasispecies in virology, see Holland, 2006). The scientific presentations were opened by Weissmann and Domingo, who were the last and first authors of the 1978 paper, respectively. Their talks conveyed the scientific atmosphere of the 1970s—when molecular biology was carried out with few recombinant‐DNA techniques—to a young audience. Nucleic‐acid sequencing was in … [1]: /embed/graphic-1.gif

The viral quasispecies represent a set of related variants in a virus population (e.g. from an infected patient) that contain similar mutations due to the rapid and mutation-prone replications in viruses. The characterization of viral quasispecies in a highly divergent virus population is of great interest in biomedical research, in particular, to identify virulent and drug-resistant mutations in viral genomes for diagnosis of infectious diseases and targeted drug design. In recent years, next-generation sequencing (NGS) techniques have been widely used for deep sequencing of virus populations, in an attempt to characterize low abundant viral quasispecies containing specific mutations associated with virulence or drug-resistance. However, because of the short length of NGS reads, it remains a challenge to reconstruct viral quasispecies from NGS sequencing data. In this paper, we formulate the viral quasispecies reconstruction as the vertex coloring problem on a read conflict graph, and then apply heuristic algorithms to solve it. We compared our new algorithms with one existing software tool on three simulated datasets for HIV quasispecies reconstruction. The results showed our methods can improve the accuracy on the inference of the identities and quantities of viral quansispecies in a virus population.

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

As Next Generation Sequencing (NGS) technologies continue to expand rapidly, the need to assemble and manipulate NGS data, available in the form of short genomic reads, remains the primary source of biological data in many Bioinformatics applications. As a result, many assemblers have been developed to assemble NSG short reads into long genomic sequences or contigs ready for advanced analysis such as Whole Genome Wide Studies (GWAS). However, the lack of high levels of robustness and reproducibility continue to limit the impact of Bioinformatics research and many biomedical researchers remain skeptical of results obtained from bioinformatics applications. In this study, we conduct a comparative study of various widely used assemblers and compare their performances using several NGS datasets associated with various organisms. We highlight the advantages and disadvantage of each assembler and explore the factors that impact the performance of each approach. In addition, we survey the assembly-free compression approach recently developed to process NGS short reads to analyze their performance in comparing genomic sequences represented by sets of short reads. We use phylogeny trees obtained from simulated and real datasets to evaluate the accuracy of each assembly-free approach. We test the hypothesis that non-assembly approaches could potentially overcome the limitations and inaccuracies of assembly approaches in comparing sequences, especially for large read sizes. Moreover, we proposed a hybrid approach by integrating both assembly and non-assembly approach for classifying genomic sequences. The proposed approach incorporates results obtained from partially assembling short reads as input for assembly-free methods to complete the NGS manipulation process. Preliminary superior results show that the hybrid approach is potential in comparing genomic sequences.

BackgroundNext-generation sequencing (NGS) offers a unique opportunity for high-throughput genomics and has potential to replace Sanger sequencing in many fields, including de-novo sequencing, re-sequencing, meta-genomics, and characterisation of infectious pathogens, such as viral quasispecies. Although methodologies and software for whole genome assembly and genome variation analysis have been developed and refined for NGS data, reconstructing a viral quasispecies using NGS data remains a challenge. This application would be useful for analysing intra-host evolutionary pathways in relation to immune responses and antiretroviral therapy exposures. Here we introduce a set of formulae for the combinatorial analysis of a quasispecies, given a NGS re-sequencing experiment and an algorithm for quasispecies reconstruction. We require that sequenced fragments are aligned against a reference genome, and that the reference genome is partitioned into a set of sliding windows (amplicons). The reconstruction algorithm is based on combinations of multinomial distributions and is designed to minimise the reconstruction of false variants, called in-silico recombinants.ResultsThe reconstruction algorithm was applied to error-free simulated data and reconstructed a high percentage of true variants, even at a low genetic diversity, where the chance to obtain in-silico recombinants is high. Results on empirical NGS data from patients infected with hepatitis B virus, confirmed its ability to characterise different viral variants from distinct patients.ConclusionsThe combinatorial analysis provided a description of the difficulty to reconstruct a quasispecies, given a determined amplicon partition and a measure of population diversity. The reconstruction algorithm showed good performance both considering simulated data and real data, even in presence of sequencing errors.

Long-read sequencing in ecology and evolution: Understanding how complex genetic and epigenetic variants shape biodiversity.

MotivationAs RNA viruses mutate and adapt to environmental changes, often developing resistance to anti-viral vaccines and drugs, they form an ensemble of viral strains––a viral quasispecies. While high-throughput sequencing (HTS) has enabled in-depth studies of viral quasispecies, sequencing errors and limited read lengths render the problem of reconstructing the strains and estimating their spectrum challenging. Inference of viral quasispecies is difficult due to generally non-uniform frequencies of the strains, and is further exacerbated when the genetic distances between the strains are small.ResultsThis paper presents TenSQR, an algorithm that utilizes tensor factorization framework to analyze HTS data and reconstruct viral quasispecies characterized by highly uneven frequencies of its components. Fundamentally, TenSQR performs clustering with successive data removal to infer strains in a quasispecies in order from the most to the least abundant one; every time a strain is inferred, sequencing reads generated from that strain are removed from the dataset. The proposed successive strain reconstruction and data removal enables discovery of rare strains in a population and facilitates detection of deletions in such strains. Results on simulated datasets demonstrate that TenSQR can reconstruct full-length strains having widely different abundances, generally outperforming state-of-the-art methods at diversities 1–10% and detecting long deletions even in rare strains. A study on a real HIV-1 dataset demonstrates that TenSQR outperforms competing methods in experimental settings as well. Finally, we apply TenSQR to analyze a Zika virus sample and reconstruct the full-length strains it contains.Availability and implementationTenSQR is available at https://github.com/SoYeonA/TenSQR.Supplementary informationSupplementary data are available at Bioinformatics online.

Integrative Short Reads NAvigator (ISRNA) is an online toolkit for analyzing high-throughput small RNA sequencing data. Besides the high-speed genome mapping function, ISRNA provides statistics for genomic location, length distribution and nucleotide composition bias analysis of sequence reads. Number of reads mapped to known microRNAs and other classes of short non-coding RNAs, coverage of short reads on genes, expression abundance of sequence reads as well as some other analysis functions are also supported. The versatile search functions enable users to select sequence reads according to their sub-sequences, expression abundance, genomic location, relationship to genes, etc. A specialized genome browser is integrated to visualize the genomic distribution of short reads. ISRNA also supports management and comparison among multiple datasets. ISRNA is implemented in Java/C++/Perl/MySQL and can be freely accessed at http://omicslab.genetics.ac.cn/ISRNA/.

Despite recent treatment advances, the majority of patients with chronic hepatitis C fail to respond to antiviral therapy. Although the genetic basis for this resistance is unknown, accumulated evidence suggests that changes in the heterogeneous viral population (quasispecies) may be an important determinant of viral persistence and response to therapy. Sequences within hepatitis C virus (HCV) envelope 1 and envelope 2 genes, inclusive of the hypervariable region 1, were analyzed in parallel with the level of viral replication in serial serum samples obtained from 23 patients who exhibited different patterns of response to therapy and from untreated controls. Our study provides evidence that although the viral diversity before treatment does not predict the response to treatment, the early emergence and dominance of a single viral variant distinguishes patients who will have a sustained therapeutic response from those who subsequently will experience a breakthrough or relapse. A dramatic reduction in genetic diversity leading to an increasingly homogeneous viral population was a consistent feature associated with viral clearance in sustained responders and was independent of HCV genotype. The persistence of variants present before treatment in patients who fail to respond or who experience a breakthrough during therapy strongly suggests the preexistence of viral strains with inherent resistance to IFN. Thus, the study of the evolution of the HCV quasispecies provides prognostic information as early as the first 2 weeks after starting therapy and opens perspectives for elucidating the mechanisms of treatment failure in chronic hepatitis C.

Viral quasispecies are heterogenous mixtures of viral strains generated as RNA viruses mutate and adapt to environmental changes. High-throughput DNA sequencing enables reconstruction of viral quasispecies and estimation of their abundances, thus providing information that assists in the development of effective antiviral drugs and vaccines. In this paper, sequencing data is represented by means of a binary tensor and the viral strains discovery is formulated as the tensor factorization problem. Performance of the proposed scheme is discussed. Results demonstrate effectiveness of the proposed algorithm.

An RNA virus population does not consist of a single genotype; rather, it is an ensemble of related sequences, termed quasispecies. Quasispecies arise from rapid genomic evolution powered by the high mutation rate of RNA viral replication. Although a high mutation rate is dangerous for a virus because it results in nonviable individuals, it has been hypothesized that high mutation rates create a 'cloud' of potentially beneficial mutations at the population level, which afford the viral quasispecies a greater probability to evolve and adapt to new environments and challenges during infection. Mathematical models predict that viral quasispecies are not simply a collection of diverse mutants but a group of interactive variants, which together contribute to the characteristics of the population. According to this view, viral populations, rather than individual variants, are the target of evolutionary selection. Here we test this hypothesis by examining the consequences of limiting genomic diversity on viral populations. We find that poliovirus carrying a high-fidelity polymerase replicates at wild-type levels but generates less genomic diversity and is unable to adapt to adverse growth conditions. In infected animals, the reduced viral diversity leads to loss of neurotropism and an attenuated pathogenic phenotype. Notably, using chemical mutagenesis to expand quasispecies diversity of the high-fidelity virus before infection restores neurotropism and pathogenesis. Analysis of viruses isolated from brain provides direct evidence for complementation between members in the quasispecies, indicating that selection indeed occurs at the population level rather than on individual variants. Our study provides direct evidence for a fundamental prediction of the quasispecies theory and establishes a link between mutation rate, population dynamics and pathogenesis.

RNA viruses exhibit a high mutation rate and thus they exist in infected cells as a population of closely related strains called viral quasispecies. The viral quasispecies assembly problem asks to characterize the quasispecies present in a sample from high-throughput sequencing data. We study the de novo version of the problem, where reference sequences of the quasispecies are not available. Current methods for assembling viral quasispecies are either based on overlap graphs or on de Bruijn graphs. Overlap graph-based methods tend to be accurate but slow, whereas de Bruijn graph-based methods are fast but less accurate. We present viaDBG, which is a fast and accurate de Bruijn graph-based tool for de novo assembly of viral quasispecies. We first iteratively correct sequencing errors in the reads, which allows us to use large k-mers in the de Bruijn graph. To incorporate the paired-end information in the graph, we also adapt the paired de Bruijn graph for viral quasispecies assembly. These features enable the use of long-range information in contig construction without compromising the speed of de Bruijn graph-based approaches. Our experimental results show that viaDBG is both accurate and fast, whereas previous methods are either fast or accurate but not both. In particular, viaDBG has comparable or better accuracy than SAVAGE, while being at least nine times faster. Furthermore, the speed of viaDBG is comparable to PEHaplo but viaDBG is able to retrieve also low abundance quasispecies, which are often missed by PEHaplo. viaDBG is implemented in C++ and it is publicly available at https://bitbucket.org/bfreirec1/viadbg. All datasets used in this article are publicly available at https://bitbucket.org/bfreirec1/data-viadbg/. Supplementary data are available at Bioinformatics online.

Unfinished Stories on Viral Quasispecies and Darwinian Views of Evolution

RNA viruses, such as human immunodeficiency virus, hepatitis C virus, influenza virus, and poliovirus replicate with very high mutation rates and exhibit very high genetic diversity. The extremely high genetic diversity of RNA virus populations originates that they replicate as complex mutant spectra known as viral quasispecies. The quasispecies dynamics of RNA viruses are closely related to viral pathogenesis and disease, and antiviral treatment strategies. Over the past several decades, the quasispecies concept has been expanded to provide an adequate framework to explain complex behavior of RNA virus populations. Recently, the quasispecies concept has been used to study other complex biological systems, such as tumor cells, bacteria, and prions. Here, we focus on some questions regarding viral and theoretical quasispecies concepts, as well as more practical aspects connected to pathogenesis and resistance to antiviral treatments. A better knowledge of virus diversification and evolution may be critical in preventing and treating the spread of pathogenic viruses.