ABSTRACT
This chapter describes various methods of gene sequencing and explores the point mutations of SARS-Cov-2 virus. Data sets with different annotations were downloaded from the NCBI virtual platform. The de novo and Illumina methods are employed to extrapolate the point mutations of COVID-19. As per the analysis of whole genome, we noticed that the similarity was 957/1330 (71.95%), the number of mismatches was 373 (28.04%), and 5 (0.37%) mutant variants were seen (analysis of data sets were done using R statistical software). Probability distributions such as Cauchy, exponential, gamma, log-normal, logistic, Poisson, and Weibull distribution models were formulated to know the variation of genetic sequences at whole genomic level. COVID-19 genetic data sets were subjected to new predictive sequencing model. The demonstrated models are found to be statistically more reliable or epoch and heavily concentrated on the analysis of high-throughput data sets to know the trend and seasonal effects of ASCII score. The formulated predictive models are very useful to know the seasonal and random effects of ASCII and Phred scores by comparing the low- and high-throughput data sets of whole genomic analysis, including transcriptomics, proteomics, and sequencing of amino acids. Correlation matrix was explored from whole genomics to know the distance of genome of different annotations. The mean distance of gene of 15.21 with an SD of 0.93 was observed in the USA and South Korea, and also it was found to be significantly correlated (augmented) with the virulence of virus and diseases pandemic at population level.
