name: Ofir Yaish PhD :Subject Computational modeling and analysis of biological systems based on high-throughput data

name: Ofir Yaish PhD supervisor: Prof. Yaron Orenstein and Dr. Nir Shlezinger :Subject Computational modeling and analysis of biological systems based on high-throughput data :Abstract The rapid growth of high-throughput experimental assays has opened unprecedented opportunities for understanding complex biological systems, but it has also introduced new challenges in data modeling and interpretation. In this seminar, I will present computational frameworks I developed for modeling and analyzing genomic systems, focusing on two distinct but complementary directions. First, I will discuss our work on CRISPR/Cas9 off-target prediction, where we generated the most comprehensive datasets of off-target sites with bulges to date and developed SWOffinder, a combinatorial search algorithm, alongside advanced machine-learning models that achieved state-of-the-art prediction accuracy across both in vitro and in cellula data. This study highlights the power of integrating large-scale data with tailored algorithms to address limitations in genome-editing safety and design. However, predictive modeling alone is insufficient for fully uncovering biological mechanisms. Therefore, in the second part, I will present F-MoDA (Fourier-based Motif Discovery in Attribution maps), a novel computational method for extracting regulatory motifs from deep-learning attribution maps. F-MoDA leverages signal processing and clustering to discover motifs more accurately, concisely, and efficiently than existing approaches. Together, these works demonstrate how computational modeling, combined with interpretable machine learning, can both enhance practical applications, such as genome editing, and advance our understanding of fundamental biological processes.