In Bioinformatics (Oxford, England)
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/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. Most existing computational methods for predicting chromatin interactions are based on in situ Hi-C and can detect chromatin interactions across the entire genome. However, they may not be optimal for predicting promoter-centered chromatin interactions.
RESULTS : We develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features such as genomic sequence, epigenetic signal, anchor distance, evolutionary features and DNA structural features to predict tissue/cell type-specific promoter-enhancer and promoter-promoter interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework and demonstrate that the proposed approach outperforms state-of-the-art deep learning methods. Moreover, the proposed approach can achieve comparable prediction performance using predefined biologically relevant tissues/cell types compared to using all tissues/cell types in the pretraining especially for predicting promoter-enhancer interactions. The prediction performance can be further improved by using computationally inferred biologically relevant tissues/cell types in the pretraining, which are defined based on the common genes in the proximity of two anchors in the chromatin interactions.
AVAILABILITY : https://github.com/lichen-lab/DeepPHiC.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Agarwal Aman, Chen Li
2022-Dec-10
