Computational analysis of transcriptome rearrangements in SARS-CoV-2

The COVID-19 disease illuminated the critical barriers in our knowledge of the coronavirus lifecycle and evolution and their importance for developing novel drugs and vaccines. Our future pandemic preparedness is a key area of efforts based on breaking those barriers with novel approaches in the re-analysis of significant amounts of data accumulated during the pandemic years. In this proposal, we will advance such approaches by developing computational tools for identifying non-canonical junctions (NJs) in the coronavirus transcriptome. As suggested by our current observations, such NJs seem to have been missed by currently available tools, are likely to be relevant for human infection and may lead to novel coronavirus proteins. We expect our proposed software to identify them reliably, to shed light on their properties and to help generate hypotheses for identifying novel mechanisms of NJ generation. Understanding these mechanisms may provide novel targets that could lead to efficacious treatments or novel vaccines, improving the overall pandemic preparedness. Our computational pipeline will be released in an open-source form to the scientific community, so that it could be used and improved by other researchers for studying other viruses and pathogens that may employ similar mechanisms of transcriptome rearrangements. This project will significantly enhance the research capacity towards sustainable research excellence of an urban campus of Rutgers-Camden. It will support active participation of graduate and undergraduate students, and such involvement is likely to generate their interest and motivation toward careers related to a biomedical field.