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1 - 2 of 2 for "Sebastian Bowyer"
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Unveiling the ghost: machine learning’s impact on the landscape of virology
The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years machine learning has become an increasingly capable technology for addressing this challenge as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction phylogenetic analysis drug discovery and more. This review explores how machine learning has been applied to and has impacted the study of viruses before addressing the strengths and limitations of its techniques and finally highlighting the next steps that are needed for the technology to reach its full potential in this challenging and ever-relevant research area.
Insights into the unique characteristics of hepatitis C virus genotype 3 revealed by development of a robust sub-genomic DBN3a replicon
Hepatitis C virus (HCV) is an important human pathogen causing 400 000 chronic liver disease-related deaths annually. Until recently the majority of laboratory-based investigations into the biology of HCV have focused on the genotype 2 isolate JFH-1 involving replicons and infectious cell culture systems. However genotype 2 is one of eight major genotypes of HCV and there is great sequence variation among these genotypes (>30 % nucleotide divergence). In this regard genotype 3 is the second most common genotype and accounts for 30 % of global HCV cases. Further genotype 3 is associated with both high levels of inherent resistance to direct-acting antiviral (DAA) therapy and a more rapid progression to chronic liver diseases. Neither of these two attributes are fully understood thus robust genotype 3 culture systems to unravel viral replication are required. Here we describe the generation of robust genotype 3 sub-genomic replicons (SGRs) based on the adapted HCV NS3-NS5B replicase from the DBN3a cell culture infectious clone. Such infectious cell culture-adaptive mutations could potentially promote the development of robust SGRs for other HCV strains and genotypes. The novel genotype 3 SGRs have been used both transiently and to establish stable SGR-harbouring cell lines. We show that these resources can be used to investigate aspects of genotype 3 biology including NS5A function and DAA resistance. They will be useful tools for these studies circumventing the need to work under the biosafety level 3 (BSL3) containment required in many countries.