Semester of Graduation
First Major Professor
Master of Science (MS)
Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impacts food security, and is a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift. We propose a novel pipeline that incorporates both the genetic sequence and the antigenic data of the hemagglutinin protein, identifies immune epitopes within the protein, and creates a multi-layered network that allows inference of antigenic drift. This method can be applied to identify new IAV strains that have distinct epitopes, and determine which IAV strains are central within the multi-layer network that explain observed diversity, and aid in the selection of robust candidates for multi-valent IAV vaccines. Our method on a smaller test dataset was able to give us an interesting cluster formation between different graph layers that can help understand results from different epitopes. Also, by increasing dataset sizes, we noticed the evolution of the H1 HA US gamma clade into multiple sub clades than can be used to programmatically interpret antigenic drift.
Embargo Period (admin only)
Saxena, Anugrah, "A Novel Multi-layered Epitopes and Genetic Sequence Network for Predicting Antigenic Phenotype of H1 Influenza A Virus in Swine" (2021). Creative Components. 802.
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Comparative and Laboratory Animal Medicine Commons, Computational Biology Commons, Computational Engineering Commons, Data Science Commons, Epidemiology Commons, Numerical Analysis and Scientific Computing Commons, Small or Companion Animal Medicine Commons, Software Engineering Commons