RCOVID19: Recurrence-based SARS-CoV-2 features using chaos game representation

Data in Brief

Jamshid Pirgazi¹  Alireza Khanteymoori²  Khosrow Khalifeh³  Mohammad Hossein Olyaee⁴ 

[1] Corresponding author.;Department of Biology, Faculty of Sciences, University of Zanjan, Zanjan, Iran;Department of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran;Faculty of Engineering, Department of Computer Engineering, University of Gonabad, Gonabad, Iran;

关键词: SARS-CoV-2;    Nonlinear analysis;    Coordinate series;    Chaos game representation;    Recurrence quantification analysis;   

DOI  :  

来源: DOAJ

【 摘 要 】

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. It was first detected in China and was rapidly spread to other countries. Several thousands of whole genome sequences of SARS-CoV-2 have been reported and it is important to compare them and identify distinctive evolutionary/mutant markers. Utilizing chaos game representation (CGR) as well as recurrence quantification analysis (RQA) as a powerful nonlinear analysis technique, we proposed an effective process to extract several valuable features from genomic sequences of SARS-CoV-2. The represented features enable us to compare genomic sequences with different lengths. The provided dataset involves totally 18 RQA-based features for 4496 instances of SARS-CoV-2.

【 授权许可】

Unknown