Machine Learning Model to Track SARS-CoV-2 Viral Mutation Evolution and Speciation Using Next-generation Sequencing Data

Abstract

RNA sequence analysis of emerging SARS-CoV-2 infection is valuable for tracking viral evolution and developing novel diagnostic tools. Furthermore, SARS-CoV-2 sequence analysis can provide insight into potential antigenic drift events that lead to strain speciation and changing clinical outcomes. In this work, we aim to develop a pipeline using next-generation sequencing (NGS) technology in addition to machine learning/bioinformatics to track the accumulation of mutations and viral evolution.