id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-266866-z98x80zj	Sohpal, Vipan Kumar	Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA	2020-09-24		.txt	text/plain	2027	139	53	Hence the purpose of the present work is to assess the genomic relationship on the basis of statistical techniques between MERS-CoV, SARS-CoV, and SARS-CoV-2 with an objective to (1) maximized value of likelihood function of nucleotide substitution models, (2) transition/transversion bias and frequencies computation using maximum likelihood (ML) technique, (3) analyze the probability rate of substitution using ML. ML of different nucleotide substitution models BIC and AICc are the most important parameters for statistical analysis of ML to analyze the biological data. It indicates ML method accurately fits of 24 different nucleotide substitution models for biological data of SARS-CoV-2, MERS-CoV, and SARS-CoV under neutral evolution. In broad, the transitional/transversional varies from 0.57 (GTR model) to 0.89 (T92 + G + I), higher values indicate proportion of invariable sites (+I) and/or rate of variation across sites (+G) are more dominating in T92 model for SARS-CoV-2, SARS-CoV, and MERSCoV biological sequence. Six different nucleotide substitution models were simulated for biological sequence data of SARS-CoV, MERS-CoV and SARS-CoV-2.	./cache/cord-266866-z98x80zj.txt	./txt/cord-266866-z98x80zj.txt