Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network
Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network
复制标题
通过密集连接的卷积神经网络整合远端和近端信息来预测基因表达
DOI：
10.1093/bioinformatics/btz562
复制
发表时间：
2020-01-15
期刊：
影响因子：
5.8
通讯作者：
Jiang, Rui
中科院分区：
文献类型：
--
作者：
Zeng, Wanwen;Wang, Yong;Jiang, Rui
动机：顺式调控元件如增强子和启动子之间的相互作用是形成背景特异性染色质结构和基因表达的主要驱动力。虽然已经有了从基因组和表观基因组信息预测基因表达的计算方法，但由于难以精确地将调节增强子连接到靶基因，因此大多数方法忽略了长距离增强子-启动子相互作用。最近，HiChIP，一种新的高通量实验方法，已经产生了全面的数据，高分辨率的启动子和远端增强子之间的相互作用。此外，大量研究表明，深度学习在表观基因组信号预测方面达到了最先进的性能，从而促进了对调控元件的理解。考虑到这两个因素，我们整合近端启动子序列和HiChIP远端增强子-启动子相互作用来准确预测基因expression.Results：我们提出DeepExpression，一个密集连接的卷积神经网络，使用启动子序列和增强子-启动子相互作用来预测基因表达。我们证明，我们的模型始终优于基线方法，不仅在二进制基因表达状态的分类，而且在连续基因表达水平的回归，在交叉验证实验和跨细胞系预测。我们发现，序列启动子信息比实验增强子信息更有信息量;同时，基因TSS周围+/- 100 kbp内的增强子-启动子相互作用是最有益的。最后，我们可视化的启动子和增强子区域的图案，并显示与已知的图案识别的序列签名的匹配。我们希望看到使用HiChIP数据在破译基因调控机制方面的广泛应用。
Motivation: Interactions among cis-regulatory elements such as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them neglect long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, HiChIP, a novel high-throughput experimental approach, has generated comprehensive data on high-resolution interactions between promoters and distal enhancers. Moreover, plenty of studies suggest that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately predict gene expression.Results: We propose DeepExpression, a densely connected convolutional neural network, to predict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms baseline methods, not only in the classification of binary gene expression status but also in regression of continuous gene expression levels, in both cross-validation experiments and cross-cell line predictions. We show that the sequential promoter information is more informative than the experimental enhancer information; meanwhile, the enhancer-promoter interactions within +/- 100 kbp around the TSS of a gene are most beneficial. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures with known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.