20/11/2013 - 12:00 - Auditori PRBB

How do RNA-binding proteins find their targets?

Scientific sessions, CRG Group Leader Seminars

Quaid Morris

Gene Regulation, Stem Cells & Cancer, CRG

Biographical sketch:

Quaid Morris is an associate professor in the Donnelly Centre at the University of Toronto in Canada who is visiting the CRG until July 2014. He is a multi-disciplinary researcher with cross-appointments in the Departments of Computer Science, Engineering, and Molecular Genetics. He founded his lab in 2005 after having received his PhD from the Massachusetts Institute of Technology (MIT) in 2003. His doctoral training was in machine learning and computational neuroscience under the supervision of Peter Dayan (with some help from Geoffrey Hinton) at MIT and the Gatsby Unit at University College London. His undergraduate training was in computer science and biology at the University of Toronto. His lab uses statistical learning to make biological discoveries. He is currently interested in post-transcriptional regulation, automated prediction of gene function (see, and understanding cancer and other complex diseases using genomics.


Morris will talk about his laboratory’s interest in building networks describing post-transcriptional gene regulation in metazoa. A large part of this effort is computationally defining RNA-binding protein (RBP) target sites in mRNAs. The strategy to do this combines biochemistry (in collaboration with Timothy Hughes) and computation. To date, they have directly measured motifs for more than 200 RBPs and inferred motifs for nearly 5,000 more RBPs by homology. This effort is continuing with the goal of defining motifs for all metazoan RBPs with conserved RNA-binding domains (i.e., RRMs and KHs domains). The lab has also developed computational methods to infer and summarize RBP sequence and structure binding preferences from these in vitro binding data as well as increasingly available in vivo binding data. This initial work suggests that RBP structure binding preferences may be more complex than previously suspected.