DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution
DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution
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DeepHiC：用于增强 Hi-C 数据分辨率的生成对抗网络
DOI：
10.1371/journal.pcbi.1007287
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发表时间：
2020-02-01
影响因子：
4.3
通讯作者：
Bo, Xiaochen
中科院分区：
文献类型：
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作者：
Hong, Hao;Jiang, Shuai;Bo, Xiaochen
Hi-C通常用于研究三维基因组组织。然而，由于高测序成本和技术限制，大多数Hi-C数据集的分辨率是粗糙的，导致信息和生物可解释性的损失。在这里，我们开发了DeepHiC，这是一种生成对抗网络，可以从低覆盖率的测序数据中预测高分辨率的Hi-C接触图。我们证明了DeepHiC能够从低至1%的下采样读取中再现高分辨率Hi-C数据。通过对抗训练，我们的方法可以恢复类似于高分辨率Hi-C矩阵的细粒度细节，提高染色质环识别和TADs检测的准确性，并在预测准确性方面优于最先进的方法。最后，将DeepHiC应用于小鼠胚胎发育的Hi-C数据可以促进染色质环检测。我们开发了一个基于网络的工具（DeepHiC，http://sysomics.com/ deephic），允许研究人员只需点击几下就可以增强他们自己的Hi-C数据。
Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/ deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.