Data-Driven Whole-Genome Clustering to Detect Geospatial, Temporal, and Functional Trends in SARS-CoV-2 Evolution
DescriptionCurrent methods for defining SARS-CoV-2 lineages ignore the vast majority of the SARS-CoV-2 genome. We develop and apply an exhaustive vector comparison that directly compares all known SARS-CoV-2 genome sequences to produce novel lineage classifications. We utilize data-driven models that (i) accurately capture the complex interactions across the set of all known SARS-CoV-2 genomes, (ii) scale to leadership-class computing systems, and (iii) enable tracking how such strains evolve geospatially over time. Analyses of this kind may produce actionable insights and transform our ability to prepare for and respond to current and future biological threats.
TimeTuesday, June 2714:30 - 15:00 CEST
Chemistry and Materials