ARRA: REU Exon Splice Pattern Characterization of the Whole mRNA Transcriptome

ABI: Exon Splice Pattern Characterization of the \Vho1e mRNA Transcri ptome Synopsis: ~ext generation sequencing-by-synthesis technologies adapted for transcriptional profiling applications have the potential to identify and differentiate all mR~ A splice variants in a sample's transcriptome. while concurrently providing a direct quantitative measurement of their steady state level. This proposal requests support to develop novel analytical methods that will identify and align R~A sequencing tags that bridge exon splice junctions and accurately predict mR.';"A structure with much greater sensitivity than is currently possible. The proposed methods will characterize exon splice junctions directly by analyzing the nucleotide sequence of the tag in comparison to the corresponding reference genome, using algorithms that do not rely on the existing accuracy of mR~A structural annotation. ~ovel splice junctions will be identified and assessed with the same efficiency as splice junctions that are already well characterized. The methods will enable alternative mRNA splicing variants to be identified and quantified. Comparative tissue analyses will be used as a test application of the analytical methods. Intellectual Merit: Analytical and computational methods will be developed to interrogate the mR~A transcriptome using next generation deep sequencing technologies. These computational methods are: • Identification of splice junction tags (tags that bridge exon junctions) and determination of expression intensity levels for exons and mRNA splicing patterns genome-wide. • Representation of the structure and expression intensity level of all exon splicing patterns using Intensity-Weighted Splice Graph (WSG). • An optimization algorithm to reconstruct different mRNA splice variants using vVSG. • Approaches for analyzing tissue-specific splicing patterns and for identifying conserved 1Iltron/ exon sequences that regulate them. The project assembles an interdisciplinary and collaborative team of scientists representing computational genomics (Drs. Liu and Prins). statistical genomics (Drs. Bathke and Stromberg). and functional genomics (Dr. MacLeod). Areas of individual expertise will be highly synergistic for both the research and educational objectives of this proposal.