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.当前主流的基因组序列分类方法多基于文本处理或序列比对技术。本研究旨在借助深度学习技术，构建一款基于图像的基因组序列分类器。1990年，H·J·杰弗里（H J Jeffry）提出了混沌游戏表示法（Chaos Game Representation, CGR），该方法可将长一维序列转换为二维图像。本数据集包含五类严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）受关注变异株（Variants Of Concern, VOC）的基因组序列对应的CGR图像，分别为阿尔法（Alpha）、贝塔（Beta）、德尔塔（Delta）、伽马（Gamma）与奥密克戎（Omicron）。本数据集分为训练集（train）、测试集（test）与验证集（validate）三个文件夹，每个文件夹均包含五个子文件夹，对应上述五类变异株。训练集文件夹总计包含17500张图像，每个子文件夹各含3500张；测试集文件夹共收录5000张图像，每类变异株对应1000张；验证集文件夹共计2500张图像，每个类别各含500张。上述五类SARS-CoV-2 VOC的基因组序列均从GISAID数据库下载获取，随后通过Python脚本转换为CGR图像。