SUMMARY: In any population under selective pressure, a central challenge is to distinguish the genes that drive adaptation from others which, subject to population variation, harbor many neutral mutations de novo. We recently showed that such genes could be identified by supplementing information on mutational frequency with an evolutionary analysis of the likely functional impact of coding variants. This approach improved the discovery of driver genes in both lab-evolved and environmental Escherichia coli strains. To facilitate general adoption, we now developed ShinyBioHEAT, an R Shiny web-based application that enables identification of phenotype driving gene in two commonly used model bacteria, E.coli and Bacillus subtilis, with no specific computational skill requirements. ShinyBioHEAT not only supports transparent and interactive analysis of lab evolution data in E.coli and B.subtilis, but it also creates dynamic visualizations of mutational impact on protein structures, which add orthogonal checks on predicted drivers. AVAILABILITY AND IMPLEMENTATION: Code for ShinyBioHEAT is available at https://github.com/LichtargeLab/ShinyBioHEAT. The Shiny application is additionally hosted at http://bioheat.lichtargelab.org/.

MOTIVATION: Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. RESULTS: Combining coronavirus family sequence information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent. AVAILABILITY AND IMPLEMENTATION: The results of this work are made interactively available at http://cov.lichtargelab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.