RNA 3D structural alignment yields valuable information on their functional conservation and evolutionary relations. We developed two local alignment tools by formulating RNA 3D structural alignment as a graph matching problem where each node is a conserved stack pair. The first tool searches for the maximal conserved stack sets with a distance constraint and uses them to guide the alignment. It improves both the length and accuracy of the alignment. The second tool solves the rotated matching problem in RNA 3D structural alignment by searching for the maximal conserved stack sets around the rotated matched internal or junction loops. It can identify new conserved structures missed by existing tools. CRISPR-based imaging can detect the spatial and temporal features of genomic elements in the living cell. However, the existing sgRNA (single guide RNA) design tools generate a list of independent sgRNAs with insufficient binding sites in most of the genomic regions. We developed a software package to generate the optimal sgRNA sets by using integer linear programming. It significantly improved the coverage of the accessible regions across the genomes. In addition, we developed a computational pipeline to functionally characterize RNA-binding proteins by analyzing different types of sequencing data. We applied it to study two proteins and helped biologists understand their roles in translational regulation.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Chen, Xiaoli, "Computational Methods for RNA 3D Structural Alignment and Guide RNA Primer Design" (2021). Electronic Theses and Dissertations, 2020-. 1320.
Restricted to the UCF community until June 2027; it will then be open access.