Jun Wan

Title : Distinct Patterns of SARS-CoV-2 Genetic Variations

Abstract:

We explored distinct and comprehensive patterns of SARS-CoV-2 genetic variations, offering insights obtained by sophisticated analyses from diverse perspectives. Our study encompassed an in-depth examination of single nucleotide variants (SNVs), various nucleotide substitutions, and their intricate associations with the viral protein structures of SARS-CoV-2. The diverse cohort comparisons were conducted, taking into account different factors such as age, sex, geometric locations (countries), and time. This analysis aimed to provide a nuanced understanding of the evolutionary trajectories of SARS-CoV-2. We unveiled valuable insights into how the virus evolves over time and across different demographic and geographic contexts. In parallel with our research findings, we developed an interactive and user-friendly tool named the Global Evaluation of SARS-CoV-2/hCoV-19 Sequences (GESS). This platform integrates millions of high-quality complete genome sequences of SARS-CoV-2. GESS not only serves as an informative resource on the dynamic variations within the virus but also empowers users to download meticulously analyzed results. This functionality allows researchers and professionals to align the tool with their specific research needs, contributing to a more personalized and targeted exploration of SARS-CoV-2 genetic data. Furthermore, the latest version of GESS, GESS v2, came equipped with advanced capabilities. One notable feature is its in-time monitoring of newly emerging SNVs, providing a timely and comprehensive overview of concurrent mutations. This real-time monitoring proves invaluable in predicting and understanding the recombination dynamics of COVID-19. The GESS v2 also offers a web tool for calibrating mutation rates to quantify the rate at which the virus undergoes genetic changes for any specific viral genome regions. It may facilitate the identification of potentially conserved genome regions, shedding light on stable genetic elements within SARS-CoV-2. This insight holds significant promise for informing new vaccine designs, providing a foundation for targeting persistent and essential components of the virus.

Biography:

Dr. Jun Wan received PhD degree in physics at Queen’s University, Kingston, Ontario Canada, followed by a postdoctoral training in bioinformatics at Johns Hopkins University School of Medicine. In 2016, Dr. Wan moved to Indiana University School of Medicine as a tenure-track Assistant Professor, then was promoted to Associate Professor with tenure in 2022. He has broad research interests in bioinformatics and computational systems biology, especially with core motivation of understanding functional alterations of gene regulatory network from the perspectives of epigenetics, transcription, and translation. Jun has published over 130 peer-reviewed papers. He was awarded as Showalter Faculty Scholar in 2023.