Yaron Orenstein is a Senior Lecturer and the head of the Computational Biology lab at the School of Electrical and Computer Engineering at Ben-Gurion University of the Negev. Yaron completed his BSc summa cum laude in Electrical Engineering and Computer Science at Tel-Aviv University, where he continued on a direct MSc track under the supervision of Prof. Dana Ron. He then completed his PhD in Computer Science at Tel-Aviv University supervised by Prof. Ron Shamir, where he received numerous awards and fellowships, such as the Deutch prize and the Dan David fellowship. He completed his post-doctoral training at Massachusetts Institute of Technology with Prof. Bonnie Berger, and spent a semester as a Research Fellow at the Simons Institute for the Theory of Computing. In the last four and a half years, Yaron has been the head of a fruitful and productive lab with numerous publications, grants, and graduating students. He authored more than 40 journal manuscripts and conference proceedings papers, received grants from the ISF, BSF, NIH, ICA, and IIA, and mentored more than 15 graduate students. His main research interests include sequence design problems and application of deep neural networks in genomics.
Abstract
Protein-RNA interactions play vital roles in many cellular processes, and as a result are the main focus of many biological studies. Biologists would like to efficiently measure protein-RNA interactions in high-throughput, and based on these high-throughput experimental measurements train accurate machine-learning models to predict interactions to new RNA sequences. In the talk, I will present solutions to both challenges: design of efficient high-throughput experiments, and training highly accurate predictive models on high-throughput genomic data. First, I will present DeCoDe, a new method based on Integer Linear Programming to design protein-coding templates to efficiently cover many proteins in a single high-throughput experiment. DeCoDe outperforms extant methods for the task, and newly enables features that were not possible before, such as covering variable-length proteins and optimizing globally over multiple templates. Second, I will present DeepUTR, a new method based on Deep Learning to predict mRNA degradation dynamics based on the 3’-UTR sequence of an mRNA. DeepUTR outperforms extant methods for the task, and newly enables prediction of mRNA levels at various time points. Moreover, we extended the Integrated Gradients interpretability approach to handle multiple input types, and using the extended approach discovered known and novel regulatory 3’-UTR elements associated with mRNA degradation. I will conclude my talk with future plans on both sequence design problems, and deep neural networks applications in genomics.
