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

Melvin Vopson, Samuel Robson

Research output: Contribution to journal › Article › peer-review

122 Downloads (Pure)

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

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

10.1016/j.physa.2021.126383

PHYSA_S_21_00250Accepted author manuscript (Post-print), 1.25 MBLicence: CC BY-NC-ND

20 Citations
2 Article
2 Working paper
1 Patent

Entropic barcoding technology: Generating information entropy representation of data series using a sliding window and redefined symbols in M-block sub-windows
Vopson, M. (Inventor), Lukaszyk, S. (Inventor) & Vopson, A. (Inventor), 13 Nov 2024, IPC No. Publication number: GB2629903, 13 Nov. 2024 (Journal 7069), UK Intellectual Property Office, Patent No. GB2404348.1, 26 Mar 2024, Priority date 31 Mar 2023, Priority No. GB2304794
Research output: Patent

Open Access
File
A possible information entropic law of genetic mutations
Vopson, M., 8 Jul 2022, In: Applied Sciences (Switzerland). 12, 14, 8 p., 6912.
Research output: Contribution to journal › Article › peer-review

Open Access
File
219 Downloads (Pure)
Dynamics of SARS-CoV-2 genetic mutations and their information entropy
Vopson, M., 14 Jun 2022, bioRxiv, p. 1-6, 6 p.
Research output: Working paper

Open Access
File
91 Downloads (Pure)
Second law of information dynamics
Vopson, M. & Lepadatu, S., 11 Jul 2022, In: AIP Advances. 12, 7, 6 p., 075310.
Research output: Contribution to journal › Article › peer-review

Open Access
File
715 Downloads (Pure)
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
File
248 Downloads (Pure)

COVID-19 Genomics UK (COG-UK) consortium
Robson, S. (Creator), NCBI, 29 Apr 2020
https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB37886
Dataset
Dataset: GISAID EpiCov SARS-CoV-2 Whole Genome Sequencing Database
Robson, S. (Creator), GISAID, 18 Nov 2020
https://www.epicov.org/epi3/frontend#3bd481
Dataset

1 Finished
2 Active

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

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

Information Physics Institute (External organisation)
Vopson, M. (Chair), Vopson, A. (Chair) & Lepadatu, S. (Member)
15 Sept 2022
Activity: Membership types › Membership of board

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

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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A new method to study genome mutations using the information entropy

Abstract

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Keywords

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