DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.
DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.
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DeepCAPE：用于准确预测增强器的深度卷积神经网络
DOI：
10.1016/j.gpb.2019.04.006
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发表时间：
2021-08
影响因子：
9.5
通讯作者：
Jiang, Rui
中科院分区：
文献类型：
--
作者：
Chen, Shengquan;Gan, Mingxin;Lv, Hairong;Jiang, Rui
关键词：
建立横跨人类细胞的增强子景观对于破译基因调控、细胞分化和疾病发展的机制至关重要。高通量实验方法包含典型细胞系中成功报道的增强子，但要对不同细胞系特异性的增强子进行系统鉴定仍然过于昂贵和耗时。现有的计算方法，能够预测调控元件纯粹依赖于DNA序列，缺乏细胞系特异性筛选的能力。最近的研究表明，DNA片段的染色质可及性与其潜在的调控功能密切相关，因此可能为识别调控元件提供有用的信息。在上述理解的推动下，我们整合DNA序列和染色质可及性数据，以细胞系特异性的方式准确预测增强子。我们提出了DeepCAPE，这是一个深度卷积神经网络，通过DNA序列和DNA -seq数据的整合来预测增强子。得益于精心设计的特征提取机制和跳过连接策略，我们的模型不仅在细胞系特异性增强子对背景序列的不平衡分类方面始终优于现有方法，而且具有自适应不同规模数据集的能力。此外，由于采用了自编码器，我们的模型能够进行跨细胞系的预测。我们进一步可视化了第一卷积层的核，并显示了识别的序列特征与已知基序的匹配。我们最终证明了我们的模型在解释假定的疾病相关遗传变异的功能含义和区分疾病相关增强子方面的潜在能力。DeepCAPE的源代码和详细教程可在https://github.com/ShengquanChen/DeepCAPE免费获得。
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.
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