Abstract Noam Kaplan

Noam Kaplan (Israel Institute of Technology)

Title: Explicit probabilistic models for exploiting and explaining the 3D genome

In recent years, genomic methods such as Hi-C have revolutionized the study of the spatial organization of genomes and have provided key insights into a wide range of biological processes and systems. A central challenge in the study of 3D genome organization is to interpret the results of these experiments with the aim of understanding how 3D genome organization is specified and how it mediates biological function.

We have developed a unique approach to this challenge, by using generative probabilistic models that are based on explicit hypotheses about underlying biological mechanisms. These models explain various aspects of 3D genome organization as emerging from the joint action of simple molecular events. Using these models, we are able to infer underlying molecular factors, quantitively predict the effects of perturbations, and more. In addition, these models have several theoretical, technical and conceptual advantages over current heuristics used for Hi-C analysis. Finally, a few years ago we proposed that a simple version of these models can be used to repurpose Hi-C data to solve outstanding challenges in the field of genome assembly, an idea which has recently been used to assemble several high-quality major genomes.