Unraveling the evolutionary patterns and phylogenomics of coronaviruses: A consensus network approach

The COVID‐19 pandemic emphasizes the significance of studying coronaviruses (CoVs). This study investigates the evolutionary patterns of 350 CoVs using four structural proteins (S, E, M, and N) and introduces a consensus methodology to construct a comprehensive phylogenomic network. Our clustering of CoVs into 4 genera is consistent with the current CoV classification. Additionally, we calculate network centrality measures to identify CoV strains with significant average weighted degree and betweenness centrality values, with a specific focus on RaTG13 in the beta genus and NGA/A116E7/2006 in the gamma genus. We compare the phylogenetics of CoVs using our distance‐based approach and the character‐based model with IQ‐TREE. Both methods yield largely consistent outcomes, indicating the reliability of our consensus approach. However, it is worth mentioning that our consensus method achieves an approximate 5000‐fold increase in speed compared to IQ‐TREE when analyzing the data set of 350 CoVs. This improved efficiency enhances the feasibility of conducting large‐scale phylogenomic studies on CoVs.