Cell type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

Abstract

The mammalian genome is spatially organized in the nucleus to enable cell type-specific gene expression. Investigating how chromatin organization determines this specificity remains a challenge. Methods for measuring the 3D chromatin organization, such as Hi-C, are costly and bear strong technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. In this study, we present C.Origami, a deep neural network model that performs de novo prediction of cell type-specific chromatin organization. The C.Origami model enables in silico experiments to examine the impact of genetic perturbations on chromatin interactions in cancer genomes and beyond. In addition, we propose an in silico genetic screening framework that enables high-throughput identification of impactful genomic regions on 3D chromatin organization. We demonstrate that cell type-specific in silico genetic perturbation and screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulatory mechanisms in both normal and disease-related biological systems.