DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers
- Author:
Chen SHENGQUAN
1
;
Gan MINGXIN
;
Lv HAIRONG
;
Jiang RUI
Author Information
1. Ministry of Education Key Laboratory of Bioinformatics,Bioinformatics Division at the Beijing National Research Center for Information Science and Technology,Center for Synthetic and Systems Biology,Department of Automation,Tsinghua University,Beijing 100084,China
- Keywords:
Enhancer prediction;
Chromatin accessibility;
Data integration;
Transcription factor binding motif;
Disease-associated regulatory element
- From:
Genomics, Proteomics & Bioinformatics
2021;19(4):565-577
- CountryChina
- Language:Chinese
-
Abstract:
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 in-tegrate 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/Sheng-quanChen/DeepCAPE.
