ShinyBioHEAT: an interactive shiny app to identify phenotype driver genes in E.coli and B.subtilis.

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.

Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and greater coronavirus family.

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.

Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and the greater coronavirus family.

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.

Discovery of disease- and drug-specific pathways through community structures of a literature network.

Motivation: In light of the massive growth of the scientific literature, text mining is increasingly used to extract biological pathways. Though multiple tools explore individual connections between genes, diseases and drugs, few extensively synthesize pathways for specific diseases and drugs.

Results: Through community detection of a literature network, we extracted 3444 functional gene groups that represented biological pathways for specific diseases and drugs.