MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China.
Department of Statistics, Stanford University, Stanford, CA 94305, USA.
Nucleic Acids Res. 2019 Jun 4;47(10):e60. doi: 10.1093/nar/gkz167.
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.
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检测染色质接触。然而，除非在非常大量的输入细胞上进行极其深度的测序，这在技术上是有限且昂贵的，否则当前的 Hi-C 实验没有足够高的分辨率来解析调控元件之间的接触。在这里，我们开发了 DeepTACT，这是一种自举式深度学习模型，用于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-增强子相互作用，还可以推断启动子-启动子相互作用。在基于启动子捕获 Hi-C 数据的测试中，DeepTACT 优于现有方法。DeepTACT 分析还确定了一类枢纽启动子，这些启动子与细胞系之间的转录激活相关，富含管家基因，与基本生物过程功能相关，能够反映细胞相似性。最后，通过整合 GWAS 数据和 DeepTACT 预测的相互作用，我们用 IFNA2 与冠状动脉疾病的关联说明了染色质接触在人类疾病研究中的实用性。
