dc.contributor.advisor | Çiçek, Abdullah Ercüment | |
dc.contributor.author | Olgun, Gülden | |
dc.date.accessioned | 2019-09-30T11:23:29Z | |
dc.date.available | 2019-09-30T11:23:29Z | |
dc.date.copyright | 2019-09 | |
dc.date.issued | 2019-09 | |
dc.date.submitted | 2019-09-27 | |
dc.identifier.uri | http://hdl.handle.net/11693/52516 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (Ph.D.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2019. | en_US |
dc.description | Includes bibliographical references (leaves 110-135). | en_US |
dc.description.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. | en_US |
dc.description.statementofresponsibility | by Gülden Olgun | en_US |
dc.format.extent | xxii, 135 leaves : charts ; 30 cm. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Noncoding RNA | en_US |
dc.subject | miRNA | en_US |
dc.subject | lncRNA | en_US |
dc.subject | Sponge interactions | en_US |
dc.subject | Synergistic miRNA | en_US |
dc.subject | lncRNA functional enrichment | en_US |
dc.title | Discovering regulatory non-coding RNA interactions | en_US |
dc.title.alternative | Düzenleyici kodlanmayan RNA etkileşimlerinin keşfi | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | Ph.D. | en_US |
dc.identifier.itemid | B122830 | |