Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework
Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework
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使用深度学习框架从人类芯片测序数据预测调控基序
DOI：
10.1093/nar/gkz672
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发表时间：
2019-09-05
影响因子：
14.9
通讯作者：
Ma, Qin
中科院分区：
文献类型：
--
作者：
Yang, Jinyu;Ma, Anjun;Ma, Qin
转录因子结合位点和顺式调节基序的鉴定是一个领域，其中揭示了控制蛋白-DNA结合的规则。在这里，我们开发了一种使用深神经网络和二项式分布模型的新方法（深序和形状基序或DESSO），用于顺式调节基序预测。 Desso在预测690个人类编码芯片序列数据集中的图案方面优于现有工具，包括深层。此外，Desso的深度学习框架通过允许鉴定人类转录因子（TFS）中已知和新的蛋白质 - 蛋白质DNA绑定相互作用，从而将基序的发现扩展到了最新的发现。具体而言，在K562细胞系中表达的100个TF中鉴定了61个推定的束缚相互作用。在这项工作中，通过整合DNA形状特征的检测，Desso的力量进一步扩大。我们发现，形状信息具有强大的TF-DNA结合能力，并为人类TF提供了新的推定形状基序。因此，Desso通过将DNA结合的复杂性整合到深度学习框架中，改善了TF结合位点的识别和结构分析。
The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including Deep-Bind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein-protein-DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF-DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework.