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

ABSTRACT: 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.

SUBMITTER: Olyaee MH

PROVIDER: S-EPMC7411429 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

Olyaee Mohammad Hossein MH Pirgazi Jamshid J Khalifeh Khosrow K Khanteymoori Alireza A

Data in brief 20200807

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 eff ...[more]

PMID: 32835040

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