C3.ai Digital Transformation Institute Colloquium on Digital Transformation Science webinar will be Thursday, November 19 at 3:00 p.m. U.S. Central Time. Presenting "Mining Diagnostics Sequences for SARS-CoV-2 using Variation-Aware, Graph-Based Machine Learning Approaches Applied to SARS-CoV-1, SARS-CoV-2, and MERS Datasets" will be Nancy Amato from the University of Illinois at Urbana-Champaign.
Registration is required to attend this event.
Abstract: To date, the vast majority of COVID-19 genomic research has been focused on a high-level view of SARS-CoV-2 diversity, overlooking the diversity of the viral population that exists within each COVID-19 positive patient. While viral load of SARS-CoV-2 in an individual can exceed hundreds of thousands of copies, available genomic databases only contain a single consensus version of this diverse population, discarding any low-frequency mutations. The goal of our project, CoVariants, is to develop novel computational approaches to recover these discarded variants and allow for rapid characterization of within-host diversity of SARS-CoV-2 across tens of thousands of samples. Commonly used computational tools either are not designed for the detection of low-frequency variants within viral populations, or require significant computational resources per sample. In this talk, we will describe how new parallelization strategies and approximate statistical methods can reduce the computational requirements of a widely used existing approach by up to 400 percent while preserving 100 percent of the low-frequency genomic diversity. We will end our talk by highlighting how we plan to use these improved computational methods to provide insight into the biological underpinnings of SARS-CoV-2 transmissibility and severity compared to other coronaviruses.
See details of upcoming talks
Register for any upcoming talks
