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.
Keywords: consensus clustering; coronavirus evolution; phylogenomic networks.
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