Using Chaos-Game-Representation for Analysing the SARS-CoV-2 Lineages, Newly Emerging Strains and Recombinants.

Area of Science:

• Virology
• Computational Biology
• Phylogenetics

Background:

• Viruses evolve rapidly due to high mutation rates, necessitating accurate classification for evolutionary studies and therapeutics.
• Traditional phylogenetic classification relies on sequence alignment, which can be computationally intensive for whole genomes.
• Alignment-free methods are increasingly sought for efficient whole-genome comparisons.

Purpose of the Study:

• To evaluate the Chaos Game Representation (CGR) method for classifying closely related SARS-CoV-2 lineages (A and B).
• To assess the CGR method's efficiency and accuracy in distinguishing viral strains and variants, including the XBB variant.

Main Methods:

• Utilized the Chaos Game Representation (CGR) method, a statistical physics-based approach independent of sequence alignment.
• Applied CGR to whole genome sequences of SARS-CoV-2 lineages A and B.
• Developed an R pipeline, CGRphylo, integrating CGR for phylogenetic tree construction and visualization.

Main Results:

• The CGR method successfully generated the SARS-CoV-2 phylogeny for lineages A and B.
• Accurate classification of eight distinct strains and the XBB variant from parental strains was achieved.
• CGR demonstrated lower computational resource requirements, speed, and accuracy for long sequences compared to alignment-based methods.

Conclusions:

• The CGR method is effective for accurate classification and tracking of rapidly evolving viruses like SARS-CoV-2.
• This approach provides valuable insights into viral evolution, strain emergence, and recombinant development.
• The CGRphylo pipeline offers a user-friendly tool for phylogenetic analysis of viral genomes.