Vopson, M. M. | Search bioRxiv

bioRxiv2022

Dynamics of SARS-CoV-2 genetic mutations and their information entropy

We report an investigation of the mutations dynamics of the SARS-CoV-2 virus using Shannons information theory. Our study includes seventeen RNA genetic sequences collected at different geographic locations and timeframes ranging from Dec. 2019 to Oct. 2021. The data shows a previously unobserved relationship between the information entropy of genomes and their mutation dynamics. The information entropy of the mutated variants decreases linearly with the number of genetic mutations with a negative slope of 1.52 x 10-5 bits / mutations, pointing to a possible deterministic approach to the dynamics of genetic mutations. The method proposed here could be used to develop a predictive algorithm of genetic mutations.

bioinformatics↗

bioRxiv2021

A new method to study genome mutations using the information entropy

We report a non-clinical, mathematical method of studying genetic sequences based on the information theory. Our method involves calculating the information entropy spectrum of genomes by splitting them into "windows" containing a fixed number of nucleotides. The information entropy value of each window is computed using the m-block information entropy formula. We show that the information entropy spectrum of genomes contains sufficient information to allow detection of genetic mutations, as well as possibly predicting future ones. Our study indicates that the best m-block size is 2 and the optimal window size should contain more than 9, and less than 33 nucleotides. In order to implement the proposed technique, we created specialized software, which is freely available. Here we report the successful test of this method on the reference RNA sequence of the SARS-CoV-2 virus collected in Wuhan, Dec. 2019 (MN908947) and one of its randomly selected variants from Taiwan, Feb. 2020 (MT370518), displaying 7 mutations.

bioinformatics↗