Chaos game representation dataset of SARS-CoV-2 genome

Raquel de M Barbosa; Marcelo A C Fernandes

doi:10.1016/j.dib.2020.105618

Chaos game representation dataset of SARS-CoV-2 genome

Data Brief. 2020 Apr 25:30:105618. doi: 10.1016/j.dib.2020.105618. eCollection 2020 Jun.

Authors

Raquel de M Barbosa¹, Marcelo A C Fernandes^{2

3}

Affiliations

¹ MIT Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.
² Laboratory of Machine Learning and Intelligent Instrumentation, IMD/nPITI, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.
³ Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil.

Abstract

As of April 16, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 142,000 deaths and more than 2,000,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream, digital signal processing, and machine learning techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical values representation. Thus, the dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the CGR of 100 instances of SARS-CoV-2 virus, 11540 instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

Keywords: CGR; COVID-19; SARS-CoV-2.