Base-resolution prediction of transcription factor binding signals by a deep learning framework.
Base-resolution prediction of transcription factor binding signals by a deep learning framework.
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DOI：
10.1371/journal.pcbi.1009941
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
Huang DS
中科院分区：
文献类型：
--
作者：
Zhang Q;He Y;Wang S;Chen Z;Guo Z;Cui Z;Liu Q;Huang DS
转录因子（TF）在调节基因表达中发挥着重要作用，因此识别它们结合的位点已成为分子和细胞生物学的基本步骤。在本文中，我们开发了一个深度学习框架，利用现有的全卷积神经网络（FCN）来预测碱基分辨率水平的 TF-DNA 结合信号（称为 FCNsignal）。所提出的 FCNsignal 可以同时实现以下任务：（i）对结合区域的碱基分辨率信号进行建模； (ii) 区分结合或非结合区域； (iii) 定位 TF-DNA 结合区域； (iv) 预测结合基序。此外，FCNsignal 还可用于预测整个基因组的开放区域。 53 个 TF ChIP-seq 数据集和 6 个染色质可及性 ATAC-seq 数据集的实验结果表明，我们提出的框架优于一些现有的最先进方法。此外，我们探索使用训练好的FCNsignal来定位整个染色体上所有潜在的TF-DNA结合区域并预测任意长度的DNA序列，结果表明我们的框架可以找到大多数已知的结合区域并接受任意长度的序列。此外，我们通过一系列实验证明了我们的框架在发现致病相关单核苷酸多态性（SNP）方面的潜在能力。转录因子结合位点 (TFBS) 的识别是研究生物系统中基因调控网络的基础，因为 TF 通过与特定的 TFBS 结合来激活或抑制基因的转录。随着高通量测序技术和深度学习（DL）的发展，已经开发了多种基于深度学习的方法来系统地研究 TFBS，并取得了令人印象深刻的性能。然而，这些方法要么过度关注区分结合序列或非结合序列，要么单独完成多个 TFBS 相关任务。在这项工作中，我们提供了一个集成框架，它利用 FCN 架构在碱基分辨率水平上预测 TF-DNA 结合信号，以同时研究多个 TFBS 相关任务。更重要的是，我们还证明了我们提出的框架能够从任意长度的 DNA 序列中定位所有潜在的 TF-DNA 结合区域。我们希望我们的框架能够为研究 TF-DNA 结合机制及其相关任务提供新的视角。
Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framework leveraging existing fully convolutional neural networks (FCN) to predict TF-DNA binding signals at the base-resolution level (named as FCNsignal). The proposed FCNsignal can simultaneously achieve the following tasks: (i) modeling the base-resolution signals of binding regions; (ii) discriminating binding or non-binding regions; (iii) locating TF-DNA binding regions; (iv) predicting binding motifs. Besides, FCNsignal can also be used to predict opening regions across the whole genome. The experimental results on 53 TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets show that our proposed framework outperforms some existing state-of-the-art methods. In addition, we explored to use the trained FCNsignal to locate all potential TF-DNA binding regions on a whole chromosome and predict DNA sequences of arbitrary length, and the results show that our framework can find most of the known binding regions and accept sequences of arbitrary length. Furthermore, we demonstrated the potential ability of our framework in discovering causal disease-associated single-nucleotide polymorphisms (SNPs) through a series of experiments. Identification of transcription factor binding sites (TFBSs) is fundamental to study gene regulatory networks in biological systems, as TFs activate or suppress the transcription of genes by binding to specific TFBSs. With the development of high-throughput sequencing technologies and deep learning (DL), several DL-based approaches have been developed for systematically studying TFBSs, achieving impressive performance. Nevertheless, these methods either excessively focus on discriminating binding or non-binding sequences or individually accomplish multiple TFBSs-associated tasks. In this work, we provide an integrated framework, which utilizes the FCN architecture to predict TF-DNA binding signals at the base-resolution level, to simultaneously study multiple TFBSs-associated tasks. More importantly, we also demonstrate that our proposed framework has the ability to locate all potential TF-DNA binding regions from DNA sequences of arbitrary length. We hope that our framework can provide a new perspective on studying the mechanism of TF-DNA binding and its related tasks.
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DOI：
10.1093/bioinformatics/btr064
发表时间：
2011-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Grant CE;Bailey TL;Noble WS
通讯作者：
Noble WS
DOI：
10.1126/science.1162327
发表时间：
2009-06-26
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Badis G;Berger MF;Philippakis AA;Talukder S;Gehrke AR;Jaeger SA;Chan ET;Metzler G;Vedenko A;Chen X;Kuznetsov H;Wang CF;Coburn D;Newburger DE;Morris Q;Hughes TR;Bulyk ML
通讯作者：
Bulyk ML
DNA/RNA 基序挖掘中的深度学习综述
影响因子：
9.5
作者：
He Y;Shen Z;Zhang Q;Wang S;Huang DS
通讯作者：
Huang DS
影响因子：
46.9
作者：
Berger, Michael F.;Philippakis, Anthony A.;Bulyk, Martha L.
通讯作者：
Bulyk, Martha L.
影响因子：
48
作者：
Isakova, Alina;Groux, Romain;Deplancke, Bart
通讯作者：
Deplancke, Bart