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 as sequence length, percentage similarity between the sequence information,open and closed gaps in the sequence due to multiple mutations and many others. By doing this, we will be able to predict accurately the similarity of SARS CoV-2 virus 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 the taxonomy of the corona-virus family.

中文翻译：

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使用K均值无监督学习算法的SARS冠状病毒中核苷酸的基因组相似性

被发现的冠状病毒数量的急剧增加和被测序的冠状病毒基因组给了我们一个很大的机会，可以对该病毒家族进行基因组学和生物信息学分析。冠状病毒拥有所有已知RNA病毒中最大的基因组（26.4至31.7 kb），G + C含量从32％至43％不等。在系统发育上，已知存在三个属，即冠状病毒，β-冠状病毒和γ-冠状病毒，其中β-冠状病毒由A，B，C亚组组成，但现在也存在新的D属，即δ-冠状病毒。在这种情况下，对所有病毒数据进行有效分类非常重要，这样可以帮助我们进行适当的计划，控制和处理。本文的目的是根据序列长度，序列长度等参数对SARS冠状病毒核苷酸序列进行分类 序列信息之间的百分比相似性，由于多重突变和许多其他原因导致的序列中的开放和封闭缺口。这样，我们将能够准确预测SARS CoV-2病毒与其他冠状病毒（如武汉冠状病毒，蝙蝠冠状病毒和肺炎病毒）的相似性，并有助于我们更好地了解冠状病毒家族的分类。