Thind Amarinder Singh, Sinha Somdatta
Department of Biological Sciences, Indian Institute of Science Education & Research, Mohali, India.
Illawarra Shoalhaven Local Health District (ISLHD), NSW Health, Australia.
Curr Genomics. 2023 Nov 22;24(3):187-195. doi: 10.2174/0113892029264990231013112156.
Viruses have high mutation rates, facilitating rapid evolution and the emergence of new species, subspecies, strains and recombinant forms. Accurate classification of these forms is crucial for understanding viral evolution and developing therapeutic applications. Phylogenetic classification is typically performed by analyzing molecular differences at the genomic and sub-genomic levels. This involves aligning homologous proteins or genes. However, there is growing interest in developing alignment-free methods for whole-genome comparisons that are computationally efficient.
Here we elaborate on the Chaos Game Representation (CGR) method, based on concepts of statistical physics and free of sequence alignment assumptions. We adopt the CGR method for classification of the closely related clades/lineages A and B of the SARS-Corona virus 2019 (SARS-CoV-2), which is one of the fastest evolving viruses.
Our study shows that the CGR approach can easily yield the SARS-CoV-2 phylogeny from the available whole genomes of lineage A and lineage B sequences. It also shows an accurate classification of eight different strains and the newly evolved XBB variant from its parental strains. Compared to alignment-based methods (Neighbour-Joining and Maximum Likelihood), the CGR method requires low computational resources, is fast and accurate for long sequences, and, being a K-mer based approach, allows simultaneous comparison of a large number of closely-related sequences of different sizes. Further, we developed an R pipeline CGRphylo, available on GitHub, which integrates the CGR module with various other R packages to create phylogenetic trees and visualize them.
Our findings demonstrate the efficacy of the CGR method for accurate classification and tracking of rapidly evolving viruses, offering valuable insights into the evolution and emergence of new SARS-CoV-2 strains and recombinants.
病毒具有高突变率，这促进了快速进化以及新物种、亚种、毒株和重组形式的出现。对这些形式进行准确分类对于理解病毒进化和开发治疗应用至关重要。系统发育分类通常通过分析基因组和亚基因组水平的分子差异来进行。这涉及比对同源蛋白质或基因。然而，人们越来越关注开发用于全基因组比较的无比对方法，这些方法计算效率高。
在此，我们详细阐述基于统计物理概念且无需序列比对假设的混沌游戏表示（CGR）方法。我们采用CGR方法对2019年严重急性呼吸综合征冠状病毒（SARS-CoV-2）的密切相关分支A和B进行分类，SARS-CoV-2是进化最快的病毒之一。
我们的研究表明，CGR方法能够轻松地从分支A和分支B序列的可用全基因组中得出SARS-CoV-2系统发育。它还准确地对八个不同毒株以及新进化的XBB变体与其亲本毒株进行了分类。与基于比对的方法（邻接法和最大似然法）相比，CGR方法所需计算资源少，对长序列快速且准确，并且作为基于k-mer的方法，允许同时比较大量不同大小的密切相关序列。此外，我们开发了一个R管道CGRphylo，可在GitHub上获取，它将CGR模块与其他各种R包集成，以创建系统发育树并进行可视化。
我们的研究结果证明了CGR方法在准确分类和追踪快速进化病毒方面的有效性，为新的SARS-CoV-2毒株和重组体的进化及出现提供了有价值的见解。
