A Novel Natural Graph for Efficient Clustering of Virus Genome Sequences

Background: This study addresses the need for analyzing viral genome sequences and understanding their genetic relationships. The focus is on introducing a novel natural graph approach as a solution. Objective: The objective of this study is to demonstrate the effectiveness and advantages of the proposed natural graph approach in clustering viral genome sequences into distinct clades, subtypes, or districts. Additionally, the aim is to explore its interpretability, potential applications, and implications for pandemic control and public health interventions. Methods: The study utilizes the proposed natural graph algorithm to cluster viral genome sequences. The results are compared with existing methods and multidimensional scaling to evaluate the performance and effectiveness of the approach. Results: The natural graph approach successfully clusters viral genome sequences, providing valuable insights into viral evolution and transmission dynamics. The ability to generate directed connections between nodes enhances the interpretability of the results, facilitating the investigation of transmission pathways and viral fitness. Conclusion: The findings highlight the potential applications of the natural graph algorithm in pandemic control, transmission tracing, and vaccine design. Future research directions may involve scaling up the analysis to larger datasets and incorporating additional genetic features for improved resolution. The natural graph approach presents a promising tool for viral genomics research with implications for public health interventions.