A new method to study genome mutations using the information entropy

Melvin Vopson; Samuel Robson

doi:10.1016/j.physa.2021.126383

A new method to study genome mutations using the information entropy

Research output: Contribution to journal › Article › peer-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 language	English
Article number	126383
Number of pages	9
Journal	Physica A: Statistical Mechanics and its Applications
Volume	584
Early online date	31 Aug 2021
DOIs	https://doi.org/10.1016/j.physa.2021.126383
Publication status	Published - 15 Dec 2021

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.physa.2021.126383

Embargoed Document

PHYSA_S_21_00250
Accepted author manuscript (Post-print), 1.25 MB
Licence: CC BY-NC-ND
Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 31/08/22

2 Citations
1 Working paper

A new method to study genome mutations using the information entropy
Vopson, M. & Robson, S., 27 May 2021, bioRxiv, 12 p.
Research output: Working paper

Open Access
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3 Active

GENIES: Mapping the information entropy spectrum of genomes
Vopson, M. & Robson, S.
1/05/20 → …
Project: Research

File
STOP COVID-19: Sequencing and Tracking Of Phylogeny in COVID-19
Robson, S., Scarlett, G., Bourgeois, Y., Beckett, A., Loveson, K., Glaysher, S., Chauhan, A., Goudarzi, S., Cook, K., Fearn, C., Paul, H. & Dent, H.
COG-UK, Public Health England
1/04/20 → 31/03/23
Project: Research
Applications of Information Theory to fundamental Physics
Vopson, M. & Robson, S.
Engineering and Physical Sciences Research Council
3/06/19 → …
Project: Research

Could a mathematical application of information theory identify and predict SARS-CoV-2 mutations?
Melvin Vopson
31/05/21
1 item of Media coverage
Press/Media: Research

Cite this

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title = "A new method to study genome mutations using the information entropy",

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.",

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AB - 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.

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KW - detection of genetic mutations

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A new method to study genome mutations using the information entropy

Abstract

Keywords

UN SDGs

Access to Document

Embargoed Document

Fingerprint

Research output

A new method to study genome mutations using the information entropy

Projects

GENIES: Mapping the information entropy spectrum of genomes

STOP COVID-19: Sequencing and Tracking Of Phylogeny in COVID-19

Applications of Information Theory to fundamental Physics

Press / Media

Could a mathematical application of information theory identify and predict SARS-CoV-2 mutations?

Cite this