Abstract
Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.
Highlights
Chromatin contacts between regulatory elements are widely studied to interpret the regulation relationship of transcriptome and to understand the regulatory mechanism of complex diseases
We developed a deep learning model, named EnContact, to identify E-E interactions using features learned from genomic sequences
Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms
Summary
Chromatin contacts between regulatory elements are widely studied to interpret the regulation relationship of transcriptome and to understand the regulatory mechanism of complex diseases. Chromosome conformation capture (3C)-based methods, including 4C and 5C, have been developed to detect physical contacts on a local scale (Dekker et al, 2002; Simonis et al, 2006; Dostie et al, 2006). Hi-C, Capture Hi-C, and HiChIP techniques allow genome-wide detection of interactions between all possible pairs of regions (Rao et al, 2014; Mifsud et al, 2015; Mumbach et al, 2016), which provides the. EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model. All of these techniques require an extremely deep sequencing depth to achieve high resolution, which can hardly be applied to a large number of cell lines. Computational approaches are needed to help with the identification of finer-mapping interactions
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