Cell type-specific prediction of 3D chromatin architecture

• Published in: bioRxiv
• Main Authors: Tan, Jimin, Rodriguez-Hernaez, Javier, Sakellaropoulos, Theodore, Boccalatte, Francesco, Aifantis, Iannis, Skok, Jane, Fenyo, David, Xia, Bo, Tsirigos, Aristotelis
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 07.03.2022
• Subjects: Chromatin; Gene expression; Genomes; Neural networks; Nucleotide sequence
• DOI: 10.1101/2022.03.05.483136

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 bear 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. Competing Interest Statement A.T. is a scientific advisor to Intelligencia AI. I.A. is a consultant for Foresite Labs. J.T, B.X and A.T are inventors on a filed patent covering the models and tools reported herein. All other authors declare no competing interests.