School of Informatics, Xiamen University, Xiamen 361005, China.
Graduate School, Yunnan Minzu University, Kunming 650504, China.
Curr Pharm Des. 2021;27(15):1847-1855. doi: 10.2174/1381612826666201124112710.
Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.
人类基因组中的增强子-启动子相互作用（EPIs）对于转录调控至关重要，它可以紧密控制基因表达。识别 EPIs 可以帮助我们更好地破译基因调控并理解疾病机制。然而，用于识别 EPIs 的实验方法受到资金、时间和人力的限制，而使用 DNA 序列和基因组特征的计算方法则是可行的替代方法。深度学习方法在分类方面表现出了广阔的前景，并已被用于识别 EPIs。在本调查中，我们特别关注基于序列的深度学习方法，并对文献进行了全面综述。首先，我们简要介绍了现有的 EPIs 预测基于序列的框架及其技术细节。然后，我们详细介绍了数据集、预处理方法和评估策略。最后，我们总结了这些方法所面临的挑战，并提出了一些未来的机会。我们希望本综述将为进一步研究增强子-启动子相互作用提供有用的参考。
