Cell type-specific prediction of 3D chromatin architecture

Abstract

The mammalian genome is spatially organized in the nucleus to enable cell type-specific gene expression. Investigating how chromatin architecture determines this specificity remains a big challenge. Methods for measuring the 3D chromatin architecture, such as Hi-C, are costly and bears strong technical limitations, restricting their widespread application particularly when concerning genetic perturbations. In this study, we present C.Origami, a deep neural network model for predicting de novo cell type-specific chromatin architecture. By incorporating DNA sequence, CTCF binding, and chromatin accessibility profiles, C.Origami achieves accurate cell type-specific prediction. C.Origami enables in silico experiments that examine the impact of genetic perturbations on chromatin interactions, and moreover, leads to the identification of a compendium of cell type-specific regulators of 3D chromatin architecture. We expect Origami – the underlying model architecture of C.Origami – to be generalizable for future genomics studies in discovering novel regulatory mechanisms of the genome.