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Updated: Dec 11, 2025

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

Mohammad Hossein Olyaee1, Jamshid Pirgazi2, Khosrow Khalifeh3

  • 1Faculty of Engineering, Department of Computer Engineering, University of Gonabad, Gonabad, Iran.

Data in Brief
|August 25, 2020
PubMed
Summary

Researchers analyzed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genomes using nonlinear techniques. This method effectively extracts distinctive evolutionary markers from viral sequences, aiding in comparative genomics.

Keywords:
Chaos game representationCoordinate seriesNonlinear analysisRecurrence quantification analysisSARS-CoV-2

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Area of Science:

  • Genomics
  • Bioinformatics
  • Virology

Background:

  • Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 pandemic.
  • Thousands of SARS-CoV-2 whole genome sequences are available for analysis.
  • Comparing these genomes is crucial for identifying evolutionary and mutant markers.

Purpose of the Study:

  • To develop an effective method for extracting valuable features from SARS-CoV-2 genomic sequences.
  • To enable comparison of genomic sequences with varying lengths.
  • To identify distinctive evolutionary/mutant markers within SARS-CoV-2.

Main Methods:

  • Utilized Chaos Game Representation (CGR).
  • Employed Recurrence Quantification Analysis (RQA), a nonlinear analysis technique.
  • Extracted 18 RQA-based features from 4496 SARS-CoV-2 genome instances.

Main Results:

  • Developed a novel process for feature extraction from viral genomes.
  • Successfully generated 18 distinct RQA-based features.
  • The method allows for the comparison of SARS-CoV-2 genomes of different lengths.

Conclusions:

  • The proposed method using CGR and RQA is effective for analyzing SARS-CoV-2 genomic data.
  • The extracted features provide valuable insights into viral evolution and mutation.
  • This approach facilitates comparative genomics of SARS-CoV-2.