Information Theory: Applications to the Study of Mutation Dynamics

Abstract

This study investigates the mutation dynamics of viral genomes using computational methods and information theory. The analysis focuses on Influenza-A virus genomes collected from Tianjin, China, between November 2009 and February 2011. The GENetic Information Entropy Spectra (GENIES) software is employed to calculate the information entropy (IE) of viral genomes and to compare them against a reference genome. The analysis reveals frequent mutation sites, with adenine (A) exhibiting the highest mutation frequency. The study provides valuable insights into the mutation patterns and dynamics of the analysed genomes, however, limitations in data size and the capabilities of the software are acknowledged, highlighting the need for further research and larger datasets to validate and expand upon these findings. Overall, this computational approach demonstrates the potential of using information theory and GENIES to enhance our understanding of viral mutation dynamics, with implications for vaccine design and preparedness for future viral strains.