DeepPHiC: Predicting promoter-centered chromatin interactions using a novel deep learning approach

Motivation Promoter-centered chromatin interactions, which include promoter-enhancer and promoter-promoter interactions, are important to decipher gene regulation and disease mechanisms. The development of next generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues or cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions.

Results To overcome these challenges, we develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features including genomic sequence, epigenetic signal and anchor distance to predict tissue/cell type-specific genome-wide promoter-enhancer and promoter-promoter interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework. We demonstrate that the proposed approach outperforms state-of-the-art deep learning methods and is robust to the inclusion of anchor distance as a feature. In addition, we find that the proposed approach can achieve comparable prediction performance using biologically relevant tissues/cell types compared to using all tissues/cell types especially for predicting promoter-enhancer interactions.

Availability https://github.com/lichen-lab/DeepPHiC