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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.
Complete genome characterization of human noroviruses allows comparison of minor alleles during acute and chronic infections
Human noroviruses (HuNoVs) circulate globally affect all age groups and place a substantial burden upon health services. High genetic diversity leading to antigenic variation plays a significant role in HuNoV epidemiology driving periodic global emergence of epidemic variants. Studies have suggested that immunocompromised individuals may be a reservoir for such epidemic variants but studies investigating the diversity and emergence of HuNoV variants in immunocompetent individuals are underrepresented. To address this we sequenced the genomes of HuNoVs present in samples collected longitudinally from one immunocompetent (acute infection) and one immunocompromised (chronic infection) patient. A broadly reactive HuNoV capture-based method was used to concentrate the virus present in these specimens prior to massively parallel sequencing to recover near complete viral genomes. Using a novel bioinformatics pipeline we demonstrated that persistent minor alleles were present in both acute and chronic infections and that minor allele frequencies represented a larger proportion of the population during chronic infection. In acute infection minor alleles were more evenly spread across the genome although present at much lower frequencies and therefore difficult to discern from error. By contrast in the chronic infection more minor alleles were present in the minor structural protein. No non-synonymous minor alleles were detected in the major structural protein over the short sampling period of the HuNoV chronic infection suggesting where immune pressure is variable or non-existent epidemic variants could emerge over longer periods of infection by random chance.