ArticleComputational biology and chemistry2021
A computational analysis of molecular evolution for virulence genes of zoonotic novel coronavirus (COVID-19).
Article in Computational biology and chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Zoonotic Novel coronavirus disease 2019 (COVID-19) is highly pathogenic and transmissible considered as emerging pandemic disease. The virus belongs from a large virus Coronaviridae family affect respiratory tract of animal and human likely originated from bat and homology to SARA-CoV and MERS-CoV. The virus consists of single-stranded positive genomic RNA coated by nucleocapsid protein. The rate of mutation in any virulence gene may influence the phenomenon of host radiation. We have studied the molecular evolution of selected virulence genes (HA, N, RdRP and S) of novel COVID-19. We used a site-specific comparison of synonymous (silent) and non-synonymous (amino acid altering) nucleotide substitutions. Maximum Likelihood genealogies based on differential gamma distribution rates were used for the analysis of null and alternate hypothesis. The null hypothesis was found more suitable for the analysis using Likelihood Ratio Test (LRT) method, confirming higher rate of substitution. The analysis revealed that RdRP gene had the fastest rate evolution followed by HA gene. We have also reported the new motifs for different virulence genes, which are further useful to design new detection and diagnosis kit for COVID -19.
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