| Issue |
E3S Web Conf.
Volume 419, 2023
V International Scientific Forum on Computer and Energy Sciences (WFCES 2023)
|
|
|---|---|---|
| Article Number | 02010 | |
| Number of page(s) | 7 | |
| Section | Applied IT Technologies in Energy and Industry | |
| DOI | https://doi.org/10.1051/e3sconf/202341902010 | |
| Published online | 25 August 2023 | |
Optimization of a coronavirus genus recognition procedure based on the n-gene of prototypic strains
1 IMPB RAS – Branch of Keldysh Institute of Applied Mathematics RAS, Pushchino, Russia
2 Moscow State Technical University n.a. N.E. Bauman, Moscow, Russia
* Corresponding author: maramaria@yandex.ru
The article offers a solution to the problem of fast and efficient recognition of the coronavirus genus. For this purpose, the authors apply a virus genome targeting method based on the use of a sufficiently short and conserved N-gene of the nucleocapsid protein. Comparison of the codon frequency distributions in the N-gene of the analyzed genome and a set of 67 prototypical strains corresponding to the coronavirus subgenus allows us to recognize the genus of the coronavirus. This paper proposes optimization of the genus recognition of coronavirus by eliminating a significant number of codons from the 64 codons of the genetic code (26 in one case and 57 in the other). The authors achieved 100% genus recognition efficiency in a sample of 2,051 coronavirus genomes from the GenBank database with annotated subgenus in the optimized procedure. The authors also achieved 99% confidence when using the optimized coronavirus genus recognition procedure in a total sample of 3,242 genomes.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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