Year: 2014
Type: dissertation
Abstract: MicroRNAs (miRNAs) are a class of short (21-25 nt) non-coding endogenous RNAs that mediate the\nexpression of their direct target genes post-transcriptionally. The goal of this thesis is to identify the\ntarget genes of miRNAs using computational methods. The most popular computational target prediction\nmethods rely on sequence based determinants to predict targets. However, these determinants are neither\nsufficient nor necessary to identify functional target sites,\nand commonly ignore the cellular conditions in which miRNAs interact with their targets \\emph{in vivo}.\nSince miRNAs activity reduces the steady-state\nabundance of mRNA targets, the main goal of this thesis is to augment large scale gene expression profiles as\na supplement to sequence-based computational miRNA target prediction techniques.\nWe develop two computational miRNA target prediction methods: InMiR and BayMiR; in addition, we study the interaction between miRNAs and lncRNAs using long RNA expression data.\n\nInMiR is a computational method that predicts the targets of intronic miRNAs based on the expression profiles of their host\ngenes across a large number of datasets. InMiR can also predict\nwhich host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host\ngenes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3 UTRs\nand are significantly more likely to have predicted Pol II-III promoters in their introns.\n\nWe also develop BayMiR that scores miRNA-mRNA pairs based on the endogenous footprint of miRNAs on gene expression in a genome-wide scale. BayMiR provides an ``endogenous target repression" index, that identifies the contribution of each miRNA in repressing a target gene in presence of other targeting miRNAs.\n \n\nThis thesis also addresses the interactions between miRNAs and lncRNAs. Our analysis on expression abundance of long RNA transcripts (mRNA and lncRNA) shows that the lncRNA target set of some miRNAs have relatively low abundance in the tissues that these miRNAs are highly active. We also found lncRNAs and mRNAs that shared many targeting miRNAs are significantly positively correlated, indicating that these set of highly expressed lncRNAs may act as miRNA sponges to promote mRNA regulation.
Source: TSpace (University of Toronto)
Author: Hossein Radfar
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2759337253