Computational Prediction of Target Genes of MicroRNAs

MicroRNAs (miRNAs) are a class of short (21-25 nt) non-coding endogenous RNAs that mediate the expression of their direct target genes post-transcriptionally. The goal of this thesis is to identify the target genes of miRNAs using computational methods. The most popular computational target prediction methods rely on sequence based determinants to predict targets. However, these determinants are neither sufficient nor necessary to identify functional target sites, and commonly ignore the cellular conditions in which miRNAs interact with their targets \emph{in vivo}. Since miRNAs activity reduces the steady-state abundance of mRNA targets, the main goal of this thesis is to augment large scale gene expression profiles as a supplement to sequence-based computational miRNA target prediction techniques. We 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. InMiR is a computational method that predicts the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3 UTRs and are significantly more likely to have predicted Pol II-III promoters in their introns. We 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. This 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.Ph