Characterizing collaborative transcription regulation with a graph-based deep learning approach.
Characterizing collaborative transcription regulation with a graph-based deep learning approach.
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DOI：
10.1371/journal.pcbi.1010162
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发表时间：
2022-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
人类表观基因组和转录活动已经通过许多基于序列的深度学习方法来表征，这些方法仅利用DNA序列。然而，转录因子之间相互作用，它们的协同调控活动超出了线性DNA序列。因此，利用信息丰富的3D染色质组织来研究转录因子之间的协作至关重要。我们开发了ECHO，一个基于图形的神经网络，预测染色质特征，并通过从200 bp高分辨率Micro-C接触图中整合3D染色质组织来表征它们之间的协作。ECHO预测了2，583个染色质特征，平均AUROC和AUPR显著高于最佳基于序列的模型。我们观察到，不同距离的染色质接触以不同的方式影响不同类型的染色质特征的预测，这表明了复杂和不同的协同调控机制。此外，通过基于梯度的归因方法，ECHO是可解释的。染色质接触上的属性识别与染色质特征相关的重要接触。DNA序列上的属性识别TF结合基序和TF协同结合。此外，结合接触和序列上的属性揭示了与目标序列的染色质特征预测相关的邻域中的重要序列模式。人类的转录活性受染色质特征的调节，包括转录因子结合、组蛋白修饰和DNase I超敏位点。近年来，人们提出了许多计算模型来预测DNA序列的染色质特征。然而，人类基因组具有复杂和动态的空间组织，并且染色质环形成并将基因组序列上相距较远的调控元件带入空间邻近，使得相距较远的转录因子可以相互作用。因此，为了研究染色质特征之间的协作，利用3D染色质组织是至关重要的。在这项工作中，我们提出了一个图神经网络模型来预测染色质特征，根据200 bp分辨率的Micro-C接触图，可以很好地捕捉精细尺度的染色质接触。此外，通过解释模型，我们确定了重要的染色质接触，有助于染色质特征预测，并表征这些染色质特征之间的合作，这有助于研究人员了解转录因子的协同结合机制。
Human epigenome and transcription activities have been characterized by a number of sequence-based deep learning approaches which only utilize the DNA sequences. However, transcription factors interact with each other, and their collaborative regulatory activities go beyond the linear DNA sequence. Therefore leveraging the informative 3D chromatin organization to investigate the collaborations among transcription factors is critical. We developed ECHO, a graph-based neural network, to predict chromatin features and characterize the collaboration among them by incorporating 3D chromatin organization from 200-bp high-resolution Micro-C contact maps. ECHO predicted 2,583 chromatin features with significantly higher average AUROC and AUPR than the best sequence-based model. We observed that chromatin contacts of different distances affected different types of chromatin features’ prediction in diverse ways, suggesting complex and divergent collaborative regulatory mechanisms. Moreover, ECHO was interpretable via gradient-based attribution methods. The attributions on chromatin contacts identify important contacts relevant to chromatin features. The attributions on DNA sequences identify TF binding motifs and TF collaborative binding. Furthermore, combining the attributions on contacts and sequences reveals important sequence patterns in the neighborhood which are relevant to a target sequence’s chromatin feature prediction. Human transcription activities are regulated by chromatin features including transcription factor binding, histone modification, and DNase I hypersensitive site. Recently many computational models are proposed to predict chromatin features from DNA sequence. However, human genome has a complex and dynamic spatial organization, and chromatin loops form and bring regulatory elements that lie far apart on the genomic sequence into spatial proximity so that transcription factors which bind far apart may interact with each other. Therefore, to investigate the collaborations among chromatin features, utilizing 3D chromatin organization is critical. In this work, we propose a graph neural network model to predict chromatin features in the light of 200bp-resolution Micro-C contact maps which capture fine-scale chromatin contacts well. Furthermore, by interpreting the model, we identify important chromatin contacts which contribute to chromatin feature prediction, and characterize the collaborations among these chromatin features, which helps researchers understand transcription factor collaborative binding mechanisms.
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影响因子：
12.3
作者：
Fortin JP;Hansen KD
通讯作者：
Hansen KD
DOI：
10.1038/nrg3663
发表时间：
2014-04
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Ong CT;Corces VG
通讯作者：
Corces VG
影响因子：
64.8
作者：
Jolma, Arttu;Yin, Yimeng;Taipale, Jussi
通讯作者：
Taipale, Jussi
影响因子：
7
作者：
Boyle, Alan P.;Song, Lingyun;Furey, Terrence S.
通讯作者：
Furey, Terrence S.
影响因子：
14.9
作者：
Quang D;Xie X
通讯作者：
Xie X