SRHiC: A Deep Learning Model to Enhance the Resolution of Hi-C Data
SRHiC: A Deep Learning Model to Enhance the Resolution of Hi-C Data
复制标题
SRHiC：增强 Hi-C 数据分辨率的深度学习模型
DOI：
10.3389/fgene.2020.00353
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发表时间：
2020-04-08
影响因子：
3.7
通讯作者：
Dai, Zhiming
中科院分区：
文献类型：
--
作者：
Li, Zhilan;Dai, Zhiming
Hi-C数据对于研究染色质三维结构具有重要意义。然而，由于测序成本，大多数现有Hi-C数据的分辨率通常是粗糙的。因此，如果我们能够从低覆盖率测序数据预测高分辨率Hi-C数据，这将是有帮助的。在这里，我们开发了一种基于深度学习的新颖而简单的计算方法，称为超分辨率Hi-C（SRHiC），以提高Hi-C数据的分辨率。我们在人细胞系中的Hi-C数据上验证了SRHiC。我们还通过增强其他人类和小鼠细胞类型中的Hi-C数据分辨率来评估SRHiC的泛化能力。结果表明，SRHiC的预测精度优于最先进的方法。
Hi-C data is important for studying chromatin three-dimensional structure. However, the resolution of most existing Hi-C data is generally coarse due to sequencing cost. Therefore, it will be helpful if we can predict high-resolution Hi-C data from low-coverage sequencing data. Here we developed a novel and simple computational method based on deep learning named super-resolution Hi-C (SRHiC) to enhance the resolution of Hi-C data. We verified SRHiC on Hi-C data in human cell line. We also evaluated the generalization power of SRHiC by enhancing Hi-C data resolution in other human and mouse cell types. Results showed that SRHiC outperforms the state-of-the-art methods in accuracy of prediction.