Combining data mining with rigorous whole-genome phylogenetics enables detailed comparative genomics from over 2.3 million genomes across Coronaviridae

Coronavirus outbreaks continue to pose significant public health challenges worldwide. Investigating the emergence of coronaviruses such as Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial to form a nuanced understanding of outbreaks past and present, as well as plan for potential future risks. Furthermore, as new variants continue to emerge, understanding the historical and evolutionary context of coronaviruses across the various clades within Coronaviridae can better inform potential recombination hotspots from regions of local homology across clades. This work is distinct in adopting a novel, computationally demanding, data-driven, bottom-up approach to consider the entire genomic landscape of all deposited coronaviruses at the time of writing in a single comparative framework, enabling a more comprehensive, integrative genomic understanding and contextualization of known coronaviruses. We demonstrate a general methodological approach applicable throughout evolutionary and comparative genomics, whereby we exhaustively clustered 2.32 million genomes, computationally selected thousands of strain-level representative genomes spanning the entirety of known coronaviruses, and performed rigorous multiple-sequence-alignment and phylogenetic inference and bootstrapping techniques typically considered feasible only for small-scale problems, and further probed the evolutionary relationship and regions of homology between MERS-CoV and SARS-CoV-2 within context of Coronaviridae as a whole.