Genomics Inform.  2020 Sep;18(3):e30. 10.5808/GI.2020.18.3.e30.

Computational Analysis of CoV-2, Severe Acute Respiratory Syndrome CoV & Middle East Respiratory Syndrome Genome using MEGA

• Sohpal VK

• KMID: 2506906
• DOI: http://doi.org/10.5808/GI.2020.18.3.e30

Abstract

The novel coronavirus pandemic that has originated from China and spread throughout the world in three months. Genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predecessor, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) play an important role in understanding the concept of genetic variation. In this paper, the genomic data accessed from National Center for Biotechnology Information (NCBI) through Molecular Evolutionary Genetic Analysis (MEGA) for statistical analysis. Firstly, the Bayesian information criterion (BIC) and Akaike information criterion (AICc) are used to evaluate the best substitution pattern. Secondly, the maximum likelihood method used to estimate of transition/transversions (R) through Kimura-2, Tamura-3, Hasegawa-Kishino-Yano, and Tamura-Nei nucleotide substitutions model. Thirdly and finally nucleotide frequencies computed based on genomic data of NCBI. The results indicate that general times reversible model has the lowest BIC and AICc score 347,394 and 347,287, respectively. The transition/transversions bias for nucleotide substitutions models varies from 0.56 to 0.59 in MEGA output. The average nitrogenous bases frequency of U, C, A, and G are 31.74, 19.48, 28.04, and 20.74, respectively in percentages. Overall the genomic data analysis of SARS-CoV-2, SARS, and MERS highlights the close genetic relationship.

Keyword

Middle East respiratory syndrome; Molecular Evolutionary Genetic Analysis; National Center for Biotechnology Information; SARS-CoV; SARS-CoV-2