 This paper proposes a framework that uses unsupervised machine learning algorithms to analyze and compare the genomic sequences of different strains of the SARS-CoV-2 virus. The algorithms are used to reduce the complexity of the data and visualize the differences between the strains. The authors found that the framework was able to distinguish between the major variants of the virus and could potentially be used to identify emerging variants in the future. This article was authored by Rohitaj Chandra, Charvi Bansal, Mingyu Kong and others.