Viral pathogen discovery.

Viral pathogen discovery is of critical importance to clinical microbiology, infectious diseases, and public health. Genomic approaches for pathogen discovery, including consensus polymerase chain reaction (PCR), microarrays, and unbiased next-generation sequencing (NGS), have the capacity to comprehensively identify novel microbes present in clinical samples. Although numerous challenges remain to be addressed, including the bioinformatics analysis and interpretation of large datasets, these technologies have been successful in rapidly identifying emerging outbreak threats, screening vaccines and other biological products for microbial contamination, and discovering novel viruses associated with both acute and chronic illnesses. Downstream studies such as genome assembly, epidemiologic screening, and a culture system or animal model of infection are necessary to establish an association of a candidate pathogen with disease

Deep Sequencing of Small RNAs in Tomato for Virus and Viroid Identification and Strain Differentiation

Small RNAs (sRNA), including microRNAs (miRNA) and small interfering RNAs (siRNA), are produced abundantly in plants and animals and function in regulating gene expression or in defense against virus or viroid infection. Analysis of siRNA profiles upon virus infection in plant may allow for virus identification, strain differentiation, and de novo assembly of virus genomes. In the present study, four suspected virus-infected tomato samples collected in the U.S. and Mexico were used for sRNA library construction and deep sequencing. Each library generated between 5–7 million sRNA reads, of which more than 90% were from the tomato genome. Upon in-silico subtraction of the tomato sRNAs, the remaining highly enriched, virus-like siRNA pools were assembled with or without reference virus or viroid genomes. A complete genome was assembled for Potato spindle tuber viroid (PSTVd) using siRNA alone. In addition, a near complete virus genome (98%) also was assembled for Pepino mosaic virus (PepMV). A common mixed infection of two strains of PepMV (EU and US1), which shared 82% of genome nucleotide sequence identity, also could be differentially assembled into their respective genomes. Using de novo assembly, a novel potyvirus with less than 60% overall genome nucleotide sequence identity to other known viruses was discovered and its full genome sequence obtained. Taken together, these data suggest that the sRNA deep sequencing technology will likely become an efficient and powerful generic tool for virus identification in plants and animals

What's in your next-generation sequence data? An exploration of unmapped DNA and RNA sequence reads from the bovine reference individual.

BackgroundNext-generation sequencing projects commonly commence by aligning reads to a reference genome assembly. While improvements in alignment algorithms and computational hardware have greatly enhanced the efficiency and accuracy of alignments, a significant percentage of reads often remain unmapped.ResultsWe generated de novo assemblies of unmapped reads from the DNA and RNA sequencing of the Bos taurus reference individual and identified the closest matching sequence to each contig by alignment to the NCBI non-redundant nucleotide database using BLAST. As expected, many of these contigs represent vertebrate sequence that is absent, incomplete, or misassembled in the UMD3.1 reference assembly. However, numerous additional contigs represent invertebrate species. Most prominent were several species of Spirurid nematodes and a blood-borne parasite, Babesia bigemina. These species are either not present in the US or are not known to infect taurine cattle and the reference animal appears to have been host to unsequenced sister species.ConclusionsWe demonstrate the importance of exploring unmapped reads to ascertain sequences that are either absent or misassembled in the reference assembly and for detecting sequences indicative of parasitic or commensal organisms

Clinical metagenomics.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health

Host Subtraction, Filtering and Assembly Validations for Novel Viral Discovery Using Next Generation Sequencing Data.

The use of next generation sequencing (NGS) to identify novel viral sequences from eukaryotic tissue samples is challenging. Issues can include the low proportion and copy number of viral reads and the high number of contigs (post-assembly), making subsequent viral analysis difficult. Comparison of assembly algorithms with pre-assembly host-mapping subtraction using a short-read mapping tool, a k-mer frequency based filter and a low complexity filter, has been validated for viral discovery with Illumina data derived from naturally infected liver tissue and simulated data. Assembled contig numbers were significantly reduced (up to 99.97%) by the application of these pre-assembly filtering methods. This approach provides a validated method for maximizing viral contig size as well as reducing the total number of assembled contigs that require down-stream analysis as putative viral nucleic acids.This work was supported by Wellcome Trust WT091501MAThis is the author accepted manuscript. It is currently under an indefinite embargo pending publication by PLOS

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

Development of workflow for picornavirus genome sequence analysis

Picornaviruses are small, non-enveloped, icosahedral, positive stranded RNA viruses and among the most common human pathogens. Some of the clinically important genera for humans are Enterovirus, Hepatovirus, Parechovirus and Cardiovirus. The symptoms for tthe picornaviral infections range from mild, asymptomatic to fatal disease. Threats posed to human health by these viruses is observedd in the constant outbreaks of enteroviruses and parechoviruses in the different parts of the world. Next generation sequencing provides an efficient way to detect and identify known or novel micro-organisms. Advantages of NGS are rapid sequencing methods, high-throughput process and affordable costs. On the other hand, NGS also requires advanced technical and computational skills, and creates a bottleneck owing to necessity of standardization of bioinformatic tools. It is therefore imperative to optimize and determine parameters, which provide accuracy in every stage of NGS workflow. The aim of this thesis was to develop a rapid and straightforward, user-friendly workflow for the assembly and analysis of picornaviral genomes. Chipster platform was chosen as the primary test platform. The workflow involved use of automated analysis pipelines (VirusDetect and A5 assembly pipeline), and alternative approaches, which included pre-processing of raw data, and reference-mapping or de novo assembly (Velvet and SPAdes) of picornavirus sequences. Except for de novo assembly and validation and quality assessment of final outputs, all steps were performed in Chipster. Of these approaches, VirusDetect and reference-mapping were not successful. A5 pipeline for microbial genome assembly was found to be very suited for picornavirus identification. Velvet and SPAdes also performed well, but Velvet assembler was found to more computationally exhaustive and time consuming. Quality assessment suggested that performance of SPAdes was relatively better than the performance of A5 or Velvet. As A5 pipeline does not require any parameter settings, it can be used as initila identification and contig/scaffold generation method for picornaviral sequences. Together with implementation of de novo assembler(s) on Chipster platform a novel, user-friendly NGS workflow for picornavirus sequence assembly can be established

Advancing transcriptome platforms

During the last decade of years, remarkable technological innovations have emerged that allow the direct or indirect determination of the transcriptome at unprecedented scale and speed. Studies using these methods have already altered our view of the extent and complexity of transcript profiling, which has advanced from one-gene-at-a-time to a holistic view of the genome. Here, we outline the major technical advances in transcriptome characterization, including the most popular used hybridization-based platform, the well accepted tag-based sequencing platform, and the recently developed RNA-Seq (RNA sequencing) based platform. Importantly, these next-generation technologies revolutionize assessing the entire transcriptome via the recent RNA-Seq technology

De novo identification of viral pathogens from cell culture hologenomes

<p>Abstract</p> <p>Background</p> <p>Fast, specific identification and surveillance of pathogens is the cornerstone of any outbreak response system, especially in the case of emerging infectious diseases and viral epidemics. This process is generally tedious and time-consuming thus making it ineffective in traditional settings. The added complexity in these situations is the non-availability of pure isolates of pathogens as they are present as mixed genomes or hologenomes. Next-generation sequencing approaches offer an attractive solution in this scenario as it provides adequate depth of sequencing at fast and affordable costs, apart from making it possible to decipher complex interactions between genomes at a scale that was not possible before. The widespread application of next-generation sequencing in this field has been limited by the non-availability of an efficient computational pipeline to systematically analyze data to delineate pathogen genomes from mixed population of genomes or hologenomes.</p> <p>Findings</p> <p>We applied next-generation sequencing on a sample containing mixed population of genomes from an epidemic with appropriate processing and enrichment. The data was analyzed using an extensive computational pipeline involving mapping to reference genome sets and <it>de-novo </it>assembly. In depth analysis of the data generated revealed the presence of sequences corresponding to <it>Japanese encephalitis </it>virus. The genome of the virus was also independently <it>de-novo </it>assembled. The presence of the virus was in addition, verified using standard molecular biology techniques.</p> <p>Conclusions</p> <p>Our approach can accurately identify causative pathogens from cell culture hologenome samples containing mixed population of genomes and in principle can be applied to patient hologenome samples without any background information. This methodology could be widely applied to identify and isolate pathogen genomes and understand their genomic variability during outbreaks.</p