Nature and Extent of Genetic Diversity of Dengue Viruses Determined by 454 Pyrosequencing

Applied shotgun metagenomics approach for the genetic characterization of dengue viruses

Dengue virus (DENV), an arthropod-borne virus, has rapidly spread in recent years. DENV diagnosis is performed through virus serology, isolation or molecular detection, while genotyping is usually done through Sanger sequencing of the envelope gene. This study aimed to optimize the use of shotgun metagenomics and subsequent bioinformatics analysis to detect and type DENV directly from clinical samples without targeted amplification. Additionally, presence of DENV quasispecies (intra-host variation) was revealed by detecting single nucleotide variants. Viral RNA was isolated with or without DNase-I treatment from 17 DENV (1-4) positive blood samples. cDNA libraries were generated using either a combination of the NEBNext® RNA to synthesize cDNA followed by Nextera XT DNA library preparation, or the TruSeq RNA V2 (TS) library preparation kit. Libraries were sequenced using both the MiSeq and NextSeq. Bioinformatic analysis showed complete ORFs for all samples by all approaches, but longer contigs and higher sequencing depths were obtained with the TS kit. No differences were observed between MiSeq and NextSeq sequencing. Detection of multiple DENV serotypes in a single sample was feasible. Finally, results were obtained within three days with associated reagents costs between €130-170/sample. Therefore, shotgun metagenomics is suitable for identification and typing of DENV in a clinical setting.

Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine

Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Challenges in dengue research: A computational perspective

The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South-East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues-real-time computational analysis of genetic data, phylodynamic modelling frameworks, within-host model frameworks and GPU-accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.