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Seminar: Dr. Hosna Jabbari

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Computational RNA structure prediction



Understanding the structure of an RNA molecule is important as RNA’s function is primarily determined by its structure. Experimental structure determination methods are time-consuming, expensive and in some cases infeasible. Therefore, computational RNA structure prediction has become an indispensable tool in the RNA research. Most computational efforts in RNA structure prediction focus on pseudoknot-free structures because of their simplicity, and less on the more complex pseudoknotted structures. While many small RNA secondary structures are pseudoknot-free, pseudoknots arise frequently in biologically important RNA molecules such as SARS-CoV2 (the virus responsible for the current pandemic).

There is evidence that an RNA molecule often folds into a structure with the minimum free energy (MFE). However, the general MFE structure prediction problem is NP-hard and inapproximable if pseudoknots are considered. The quest for accurate and efficient polynomial time algorithms for prediction of restricted, yet realistic pseudoknotted structures is still ongoing. In this talk, I will describe some of our results and future directions in tackling this challenge.


Dr. Hosna Jabbari is currently an assistant professor in the Department of Computer Science, University of Victoria. Prior to joining the University of Victoria, Dr. Jabbari held an assistant professorship at the University of Vermont, USA. She completed her PhD from the University of British Columbia. Her main research goal is to help diagnose and cure human diseases by developing novel diagnostics and therapeutics through research using techniques from bioinformatics, data science and computational omics. Dr. Jabbari is the founding director of the Computational Biology Research & Analytics (COBRA) lab, and a faculty fellow at the Institute of Aging and Lifelong Health.