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DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers

Shengquan Chen, Mingxin Gan, Hairong Lv, Rui Jiang
doi: https://doi.org/10.1101/398115
Shengquan Chen
1MOE Key Laboratory of Bioinformatics; Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China.
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Mingxin Gan
2Department of Management Science and Engineering, Donlinks School of Economics and Man-agement, University of Science and Technology Beijing, Beijing 100084, China.
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Hairong Lv
1MOE Key Laboratory of Bioinformatics; Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China.
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Rui Jiang
1MOE Key Laboratory of Bioinformatics; Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China.
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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, though having 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 under a variety of disease status. Existing computational methods, though 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 above 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. We demonstrate that our model not only consistently outperforms existing methods in the classification of enhancers against background sequences, but also accurately predicts enhancers across different cell lines. 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.

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Posted August 24, 2018.
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DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers
Shengquan Chen, Mingxin Gan, Hairong Lv, Rui Jiang
bioRxiv 398115; doi: https://doi.org/10.1101/398115
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DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers
Shengquan Chen, Mingxin Gan, Hairong Lv, Rui Jiang
bioRxiv 398115; doi: https://doi.org/10.1101/398115

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