Genomic Similarity of Nucleotides in SARS CoronaVirus using K-Means Unsupervised Learning Algorithm

The drastic increase in the number of coronaviruses discovered and coronavirus genomes being sequenced have given us a great opportunity to perform genomics and bioinformatics analysis on this family of viruses. Coronaviruses possess the largest genomes (26.4 to 31.7 kb) among all known RNA viruses, with G + C contents varying from 32% to 43%. Phylogenetically, three genera, Alphacoronavirus, Betacoronavirus and Gammacoronavirus, with Betacoronavirus consisting of subgroups A, B, C were known to exist but now a new genus D also exists,namely the Deltacoronavirus. In such a situation, it becomes highly important for efficient classification of all virus data so that it helps us in suitable planning,containment and treatment. The objective of this paper is to classify SARS corona-virus nucleotide sequences based on parameters such assequence length,percentage similarity between the sequence information,open and closed gaps in the sequence due to multiple mutationsand many others.By doing this,we will be able to predict accurately the similarity ofSARS CoV-2virus with respect to other corona-viruses like the Wuhan corona-virus,the bat corona-virus and the pneumonia virus and would help us better understand about thetaxonomyof the corona-virus family.