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A new method to study genome mutations using the information entropy

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@techreport{4a3a188e4af642928df8dbed09dbc331,
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.",
keywords = "information theory, genome mapping, information entropy, SARS-CoV-2, Covid-19",
author = "Melvin Vopson and Samuel Robson",
year = "2021",
month = may,
day = "27",
doi = "10.1101/2021.05.27.445958",
language = "English",
publisher = "bioRxiv",
address = "United States",
type = "WorkingPaper",
institution = "bioRxiv",

}

RIS

TY - UNPB

T1 - A new method to study genome mutations using the information entropy

AU - Vopson, Melvin

AU - Robson, Samuel

PY - 2021/5/27

Y1 - 2021/5/27

N2 - 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.

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.

KW - information theory

KW - genome mapping

KW - information entropy

KW - SARS-CoV-2

KW - Covid-19

U2 - 10.1101/2021.05.27.445958

DO - 10.1101/2021.05.27.445958

M3 - Working paper

BT - A new method to study genome mutations using the information entropy

PB - bioRxiv

ER -

ID: 28111932