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This article is cited in 4 scientific papers (total in 4 papers)
Bioinformatics
Coronavirus genus recognition based on prototype virus variants
M. B. Chaleya, V. A. Kutyrkinb a Institute of Mathematical Problems of Biology RAS, Pushchino, Russia
b Bauman Moscow State Technical University, Moscow, Russia
Abstract:
Method named as variant approach to recognizing genus of coronavirus that is based on frequency of codon distribution in viral ORF1ab and genes of structural proteins (S, M and N) was proposed in the work. This method uses modified statistics whose efficiency was demonstrated earlier for flavivirus species recognition. To recognize genus of coronavirus the variant approach considers both various combinations of several structural coronavirus genes and individual structural genes. Finally, coronavirus genus is determined in the result of analysis of all variants considered. The method proposed was developed with the help of learning sample from prototype viral variants of Alphacoronavirus, Betacoronavirus, Deltacoronavirus and Gammacoronavirus genus. Application of the variant approach to recognizing genus of coronavirus has demonstrated the approach high assurance at level of 95%. Among all variants of joint analysis, the most reliability (98%) in recognizing genus has been achieved if codon frequency of the ORF1ab was used. Variant approach has revealed a phenomenon of mosaic structure in coronavirus genomes, i.e., when the results of genus recognition for a few genes differ from final conclusion about coronavirus genus. It seems that such phenomenon reflects homologous recombinations of the genes between various species of the coronaviruses and plasticity of their genomes in evolutionary processes.
Key words:
coronavirus genome, ORF1ab, S-gene, M-gene, N-gene, statistical analysis, variant approach to recognizing coronavirus genus.
Received 28.01.2022, 09.03.2022, Published 15.03.2022
Citation:
M. B. Chaley, V. A. Kutyrkin, “Coronavirus genus recognition based on prototype virus variants”, Mat. Biolog. Bioinform., 17:1 (2022), 10–27
Linking options:
https://www.mathnet.ru/eng/mbb478 https://www.mathnet.ru/eng/mbb/v17/i1/p10
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Abstract page: | 47 | Full-text PDF : | 18 | References: | 17 |
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