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

Yan Kai, Jaclyn Andricovich, Zhouhao Zeng, Jun Zhu, Alexandros Tzatsos () and Weiqun Peng ()
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Yan Kai: George Washington University (GWU)
Jaclyn Andricovich: GWU
Zhouhao Zeng: George Washington University (GWU)
Jun Zhu: National Institute of Health
Alexandros Tzatsos: GWU
Weiqun Peng: George Washington University (GWU)

Nature Communications, 2018, vol. 9, issue 1, 1-14

Abstract: Abstract The 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.

Date: 2018
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DOI: 10.1038/s41467-018-06664-6

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