A new method to study genome mutations using the information entropy

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Article number126383
Number of pages9
JournalPhysica A: Statistical Mechanics and its Applications
Volume584
Early online date31 Aug 2021
DOIs
Publication statusEarly online - 31 Aug 2021

Keywords

  • information entropy
  • Shannon's theory
  • genetics
  • Covid-19
  • GENIES code
  • information entropy spectrum
  • RNA sequence
  • detection of genetic mutations

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