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

Mohammad Hossein Olyaee; Jamshid Pirgazi; Khosrow Khalifeh; Alireza Khanteymoori

Data in Brief (Oct 2020)

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

Mohammad Hossein Olyaee,
Jamshid Pirgazi,
Khosrow Khalifeh,
Alireza Khanteymoori

Affiliations

Mohammad Hossein Olyaee: Faculty of Engineering, Department of Computer Engineering, University of Gonabad, Gonabad, Iran; Corresponding author.
Jamshid Pirgazi: Department of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
Khosrow Khalifeh: Department of Biology, Faculty of Sciences, University of Zanjan, Zanjan, Iran
Alireza Khanteymoori: Department of Computer Engineering, University of Zanjan, Zanjan, Iran

Journal volume & issue: Vol. 32
p. 106144

Abstract

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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.

Published in Data in Brief

ISSN: 2352-3409 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics; Science: Science (General)
Website: http://www.journals.elsevier.com/data-in-brief/

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