DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.     

超级增强子研究进展

超级增强子是具有转录活性增强子的一个大簇,驱动控制细胞身份基因的表达,在发育和肿瘤等疾病发生过程中起到重要作用。和普通增强子相比,许多肿瘤细胞关键致癌基因是由超级增强子驱动,常见疾病如阿尔茨海默病等相关的变异显著富集于超级增强子。超级增强子在关键致癌基因的鉴定、疾病关联变异位点的发现等领域显示了巨大的应用潜力。本文首先概述了如何在全基因组水平进行增强子的鉴定,随后引入超级增强子的概念和鉴定方法,最后阐述了超级增强子的主要结构和功能特征,并对其在研究中的应用做了展望。