Discovering regulatory non-coding RNA interactions

Abstract

The vast majority of eukaryotic transcriptomes comprise noncoding RNAs (ncRNAs) which are not translated into proteins. Despite the accumulating evidence on the functional roles of ncRNAs, we are still far from understanding the whole spectrum of molecular functions ncRNAs can undertake and how they accomplish them. In this thesis we develop computational methods for discovering interactions among ncRNAs and tools to analyze them functionally. In the first part of the thesis, we present an integrative approach to discover long non-coding RNA (lncRNA) mediated sponge interactions where lncRNAs can indirectly regulate mRNAs expression levels by sequestering microRNAs (miRNAs), and act as sponges. We conduct partial correlation analysis and kernel independence tests on patient gene expression profiles and further refine the candidate interactions with miRNA target information. We use this approach to find sponge interactions specific to breast-cancer subtypes. We find that although there are sponges common to multiple subtypes, there are also distinct subtype-specific interactions with high prognostic potential. Secondly, we develop a method to identify synergistically acting miRNA pairs. These pairs have weak or no repression on the target mRNA when they act individually, but when together they induce strong repression of their target gene expression. We test the combinations of RNA triplets using non-parametric kernel-based interaction tests. In forming the triplets to test, we consider target predictions between the miRNAs and mRNA. We apply our approach on kidney tumor samples. The discovered triplets have several lines of biological evidence on a functional association among them or their relevance to kidney tumors. In the third part of the thesis, we focus on functional enrichment analysis of noncoding RNAs while some non-coding RNAs (ncRNAs) have been found to play critical regulatory roles in biological processes, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs set needs to be analyzed in a functional context. We develop a method that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out by using the functional annotations of the coding genes located proximally to the input ncRNAs. To demonstrate how this method could be used to gain insight into the functional importance of a list of interesting ncRNAs, we tackle different biological questions on datasets of cancer and psychiatric disorders. Particularly, we also analyze 28 different types of cancers in terms of molecular process perturbed and linked to altered lncRNA expression. We hope that the methods developed herein will help elucidate functional roles of ncRNAs and aid the development of therapies based on ncRNAs.