Computational Analysis of DNA Sequences: PRRSV Monitoring and Evolutionary Distance-Based Alignment

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is a rapidly evolving pathogen that requires continuous monitoring to support disease management in the swine industry. In the first part of this work, we present the development of a scalable PRRSV sequencing database within the Swine Disease Reporting System (SDRS). This system integrates sequence data from multiple veterinary diagnostic laboratories and enables real-time monitoring, facilitating the identification of emerging variants and trends in viral evolution. We talk about the complexities of the software and the challenges faced during its development.

In the second part, we address a fundamental challenge in computational biology by proposing a novel pairwise sequence alignment method based on optimal evolutionary distance. Unlike traditional alignment approaches that rely on fixed scoring schemes, this method incorporates substitution probabilities derived from evolutionary models, allowing alignment scoring to reflect underlying mutation processes. The approach is general and applicable to DNA sequence analysis beyond PRRSV.

Together, these contributions demonstrate how data-driven surveillance systems and model based computational methods can complement each other, advancing both applied and methodological aspects of sequence analysis.

Committee: Giovani Trevisan (major professor), Xiaoqiu Huang (major professor), Pavan Aduri, Ali Jannesari and Daniel Linhares