Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features

• DOI: 10.1038/s41467-018-06664-6
• Publication Date: 2018-10-05T13:34:16Z
• Authors: Yan Kai; Jaclyn Andricovich; Zhouhao Zeng; Jun Zhu; Alexandros Tzatsos; Weiqun Peng
• Keywords: 0301 basic medicine; CCCTC-Binding Factor; 0303 health sciences; Genome; Science; Q; Article; Chromatin; Cell Line; Epigenesis, Genetic; 03 medical and health sciences; ROC Curve; Humans; Gene Regulatory Networks

AbstractThe CCCTC-binding zinc-finger protein (CTCF)-mediated network of long-range chromatin interactions is important for genome organization and function. Although this network has been considered largely invariant, we find that it exhibits extensive cell-type-specific interactions that contribute to cell identity. Here, we present Lollipop, a machine-learning framework, which predicts CTCF-mediated long-range interactions using genomic and epigenomic features. Using ChIA-PET data as benchmark, we demonstrate that Lollipop accurately predicts CTCF-mediated chromatin interactions both within and across cell types, and outperforms other methods based only on CTCF motif orientation. Predictions are confirmed computationally and experimentally by Chromatin Conformation Capture (3C). Moreover, our approach identifies other determinants of CTCF-mediated chromatin wiring, such as gene expression within the loops. Our study contributes to a better understanding about the underlying principles of CTCF-mediated chromatin interactions and their impact on gene expression.