What is it about?

This research focuses on understanding and classifying the genetic variations in rapidly evolving viruses, specifically emphasising the SARS-CoV-2 virus responsible for COVID-19. Traditional classification methods involve comparing genetic sequences through alignment, but these methods are computationally intensive and may not be suitable for whole-genome analysis. Chaos Game Representation (CGR) doesn't rely on sequence alignment and is more computationally efficient. We applied this method to classify different lineages, strains, and recombinant forms of the SARS-CoV-2 virus. The study reveals that CGR can provide valuable insights into the virus's evolution. Compared to traditional alignment-based methods, CGR is faster, requires less computational resources, and allows the simultaneous comparison of a large number of closely related sequences.

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Why is it important?

Its significance becomes particularly pronounced during epidemic outbreaks, where overwhelmed resources contend with the deposition of thousands of viral sequences requiring extensive comparisons. The CGRphylo pipeline offers an efficient and accessible approach for researchers, even in low-resource settings, providing a rapid means to analyze and classify big datasets of viral genomes. This capability proves invaluable for timely and accurate assessments, ultimately aiding in the effective management and understanding of viral dynamics during critical epidemiological situations. The Chaos Game Representation method implemented in the CGRphylo pipeline proved effective in tracking the evolution of rapidly changing viruses.

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This page is a summary of: Using Chaos-Game-Representation for Analysing the SARS-CoV-2 Lineages, Newly Emerging Strains and Recombinants, Current Genomics, May 2023, Bentham Science Publishers,
DOI: 10.2174/0113892029264990231013112156.
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