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EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model

机译:通信:使用基于序列的深度学习模型预测增强器 - 增强器联系人

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摘要

Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.
机译:调节因素之间的染色质触点对于对转录调节的解释和对疾病机制的理解来说至关重要。然而,现有的计算方法主要关注增强剂和启动子之间的相互作用的预测,离开增强剂 - 增强剂(E-E)互动探索不佳。在这项工作中,我们开发了一种新颖的深度学习方法,名为Enhancer-Enhancer接触预测(围绕),以使用基因组序列作为输入来预测E-E触点。我们在统计上展示了使用培训集和从七个细胞系的HICHIP数据衍生的测试集来预测围绕的预测能力。我们还表明,我们的模型显着优于其他基线方法。此外,我们的模型识别来自基于区域的染色质触点的更精细的映射E-E相互作用,其中每个区域含有几种增强剂。此外,我们使用预测的E-E交互来确定一类集线器增强器,并发现集线器增强器往往跨细胞系处于活动状态。我们总结了我们的围绕模型能够通过从基因组序列自动学习的功能预测E-E交互。

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