Tez No	İndirme	Tez Künye	Durumu
400724		Inferring the binding preferences of RNA-binding proteins / Yazar:HİLAL KAZAN Danışman: DR. QUAID MORRIS Yer Bilgisi: University of Toronto / Yurtdışı Enstitü / Bilgisayar Bilimleri Ana Bilim Dalı Konu:Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol = Computer Engineering and Computer Science and Control Dizin:	Onaylandı Doktora İngilizce 2012 137 s.
Post-transcriptional regulation is carried out by RNA-binding proteins (RBPs) that bind to specic RNA molecules and control their processing, localization, stability and degradation. Experimental studies have successfully identied RNA targets associated with specic RBPs. However, because the locations of the binding sites within the targets are unknown and because RBPs recognize both sequence and structure elements in their binding sites, identication of RBP binding preferences from these data remains challenging. The unifying theme of this thesis is to identify RBP binding preferences from experimental data. First, we propose a protocol to design a complex RNA pool that represents diverse sets of sequence and structure elements to be used in an in vitro assay to eciently measure RBP binding preferences. This design has been implemented in the RNAcompete method, and applied genome-wide to human and Drosophila RBPs. We show that RNAcompete-derived motifs are consistent with established binding preferences. We developed two computational models to learn binding preferences of RBPs from large-scale data. Our rst model, RNAcontext uses a novel representation of secondary structure to infer both sequence and structure preferences of RBPs, and is optimized for use with in vitro binding data on short RNA sequences. We show that including structure information improves the prediction accuracy signicantly. Our second model, MaLaRKey, extends RNAcontext to t motif models to sequences of arbitrary length, and to incorporate a richer set of structure features to better model in vivo RNA secondary structure. We demonstrate that MaLaRKey infers detailed binding models that accurately predict binding of full-length transcripts.