Chaos Game Representation (CGR images ) of SARS-CoV-2 Variants of Concern (Alpha,Beta, Delta, Gamma and Omicron)

Currently available genome sequence classification methods are based on text or sequence alignment techniques. Our aim is to build an image-based genome sequence classifier using deep learning technique. In 1990 H J Jeffry proposed a method Chaos Game Representation (CGR), which converts long one-dimensional sequences into two-dimensional images. This dataset contains the CGR images of genomic sequences of SARS-CoV-2 Variants Of Concern (VOC) alpha, beta, delta, gamma, and omicron. The dataset is divided into three folders named train, test, and validate. Each folder contains five subfolders named alpha, beta, delta, gamma, and omicron. The "train" folder has a total of 17500 images - 3500 images in each subfolder. The "test" folder has 5000 images - 1000 from each category. The "validate" folder has 2500 images - 500 images from each individual class. Genomic sequences of the above-mentioned SARS- CoV-2-VOC were downloaded from the GISAID database and the sequences were then converted to CGR images using a python script.