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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

A non-clinical, mathematical method based on information theory is proposed to study genetic sequences, involving calculating the information entropy spectrum of genomes to detect genetic mutations and possibly predict future ones. The optimal m-block size is 2, and the optimal window size contains more than 9 and less than 33 nucleotides. The method was successfully tested on the reference RNA sequence of the SARS-CoV-2 virus and one of its variants from Taiwan, displaying 7 mutations.