Computational Methods of Inferring Context-specific MicroRNA Regulatory Networks

MicroRNA (miRNA) is a class of small endogenous non-coding RNA species, which regulate gene expression post-transcriptionally by forming imperfect basepair with the target sites primarily at the 3 prime untranslated regions of the messenger RNAs. Since the 1993 discovery of the first miRNA let-7 in worms, a vast number of studies have been dedicated to functionally characterizing miRNAs with a special emphasis on their roles in cancer. A single miRNA can potentially target ~400 distinct genes, and there are thousands of distinct endogenous miRNAs in the human genome. Thus, miRNAs are likely involved in virtually all biological processes and pathways including carcinogenesis. However, functionally characterizing miRNAs hinges on the accurate identification of their mRNA targets, which has been a challenging problem due to imperfect base-pairing and condition-specific miRNA regulatory dynamics. In this thesis, I developed several novel computational methods to elucidate the functional implications of miRNAs by inferring their context-specific regulatory roles in human transcriptional programs. Each method is established through an effective and integrative design by exploiting the static sequence-based information pertinent to existing knowledge of miRNA targeting and the dynamic information of miRNA activities using the recently available large data compendia, which interrogate genome-wide expression profiles of miRNAs and/or mRNAs across various cell conditions.Ph.D