Jan 4th, 2012
Title:Discovering recognition specificities of RNA-binding proteins
Metazoan genomes encode hundreds of RNA-binding proteins (RBPs). These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this thesis, we introduce a new probabilistic model, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2.
Hilal Kazan is a PhD candidate at the Department of Computer Science, University of Toronto. She received her B.Sc in Computer Science and Engineering from Sabanci University. Recently, she was a research intern at Microsoft Research, Cambridge, working on gene-environment interactions in asthma. Her research interests are in the area of computational biology and in particular her thesis is concerned with developing probabilistic models for discovering recognition specificities of RNA-binding proteins. Her work has been published in Nature Biotechnology, PLoS Computational Biology and Nucleic Acids Research.