Deciphering overlapping regulatory signals in viral transcripts

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The main goal of the project is to provide a comprehensive computational overview of the overlapping regulatory signals present in viral transcripts, which are crucial for all stages of the viral replication cycle. Many of them are distinctly different from the signals used in the cellular organisms and are dictated by the specific, taxonomy-specific biological features of viruses. Specifically, viruses evolve to invade the host immune system and exploit its intracellular resources. They also undergo extensive selection for replication efficiency. Furthermore, they need to be able to replicate in the cells that are under extreme stress (due to the virus invasion) and thus in extremely unusual intracellular environment. For these reasons viruses tend to exploit non-canonical regulatory processes that are very rare (or even do not exist) in cellular organisms. We therefore expect that different virus groups will employ different signals as part of their replication strategy, and there is no doubt that many, if not the majority, of such codes remain yet to be discovered.The specific scientific objectives of the project are as follows:- Develop a set of algorithms for detecting transcript regions with unusual patterns of nucleotide/codon composition. This includes features such as codon and codon pair bias, varying folding strength along the transcript, potential binding sites for RNAs and proteins, over- and under-represented sequence motifs, as well as the features pertinent to the decoding rate- Conduct a large-scale discovery of overlapping functional codes in all relevant viral genomic datasets- Provide a biological interpretation of the results and build theoretical models explaining the relevance of the discovered signals for the dynamics of mRNA translation and for co-translational protein foldingThe specific technical objectives of the project are as follows: - Develop a software pipeline for detecting and interpreting functional information in the viral transcripts.- Generate a publicly accessible database with the relevant predictions. All the regulatory signals discovered in the course of the project will be organized according to the virus type, signal location, and predicted functionality. Facilities for comparative analysis between different viruses will be provided. The database will enable researchers in the field to further study and validate our predictions.We anticipate that the proposed study will have important implications for both basic and applied research. It will help to better understand the evolutionary constraints acting on viral transcripts and will also provide valuable information for developing novel vaccines and anti-viral therapies.