An Alignment-Free Method for Analyzing COVID-19 Sequence Similarity and Evolutionary Divergence Across Global Epicenters

Abstract

In this paper, we developed an alignment-free method for examining the similarity and evolutionary divergence of COVID-19 virus sequences collected from five different countries that are epicenters of the disease, facilitating faster sequence analysis. The concept of similarity presented here is essentially a generalization of the notion of equivalence. Our method employed MATLAB programming to calculate the frequency of occurrence of all nucleotide bases from each DNA sequence, resulting in a dissimilarity matrix. In this matrix, smaller values indicate greater similarity among the COVID-19 virus sequences. The dissimilarities were then converted into a similarity matrix, and using the mathematical concept of fuzzy transitive relations, we ultimately derived a phylogenetic tree for the COVID-19 viruses. this help to trace back a common ancestor or identify evolutionary bottlenecks and divergence events and could help also to understand how different strains have evolved and spread, which has implications for tracking disease outbreaks and developing vaccines or treatments which is the ultimate aim of the research work.