DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning
DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning
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DeepTACT：通过引导深度学习预测 3D 染色质接触
DOI：
10.1093/nar/gkz167
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发表时间：
2019-06-04
影响因子：
14.9
通讯作者：
Jiang, Rui
中科院分区：
文献类型：
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作者：
Li, Wenran;Wong, Wing Hung;Jiang, Rui
摘要调控元件之间的相互作用对于理解转录调控和解释疾病机制至关重要。HI-C技术已被开发用于全基因组染色质接触的检测。然而，除非对非常大量的输入细胞进行极深的测序，否则目前的Hi-C实验没有足够高的分辨率来解决调控元件之间的联系，这在技术上是有限的，而且成本也很高。在这里，我们开发了DeepTACT，一个自举深度学习模型，来整合基因组序列和染色质可及性数据，以预测调控元件之间的染色质接触。DeepTACT不仅可以推断启动子-增强子之间的相互作用，还可以推断启动子-启动子之间的相互作用。在基于启动子捕获Hi-C数据的测试中，DeepTACT显示出比现有方法更好的性能。DeepTACT分析还发现了一类Hub启动子，它们与细胞系之间的转录激活相关，富含看家基因，在功能上与基本的生物学过程相关，并能够反映细胞的相似性。最后，通过对Gwas数据和DeepTACT预测的相互作用的综合分析，通过IFNa2与冠状动脉疾病的关联来说明染色质接触在人类疾病研究中的作用。
Abstract Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, which is technically limited and expensive, current Hi-C experiments do not have high enough resolution to resolve contacts between regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts between regulatory elements. DeepTACT can infer not only promoter–enhancer interactions, but also promoter–promoter interactions. In tests based on promoter capture Hi-C data, DeepTACT shows better performance over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are correlated with transcriptional activation across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of chromatin contacts in the study of human diseases is illustrated by the association of IFNA2 to coronary artery disease via an integrative analysis of GWAS data and interactions predicted by DeepTACT.