HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data
HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data
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DOI：
10.1093/bioinformatics/btz251
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发表时间：
2019-11-01
期刊：
影响因子：
5.8
通讯作者：
Wang, Zheng
中科院分区：
文献类型：
--
作者：
Liu, Tong;Wang, Zheng
动机：高分辨率的Hi-C数据对于千碱基水平的三维（3D）基因组组织研究是必不可少的。然而，通过进行Hi-C实验产生高分辨率的Hi-C数据（例如5kb）需要数百万个哺乳动物细胞，这可能最终产生数十亿对末端读取，并且测序成本很高。因此，利用计算方法提高高碳数据的分辨率具有重要的现实意义。结果：我们开发了一种新的计算方法，称为HiCNN，该方法使用54层极深卷积神经网络来提高Hi-C数据的分辨率。该网络同时包含全局和局部残差学习，并采用了多种加速技术，收敛速度快。我们使用实际高分辨率和计算预测高分辨率Hi-C数据之间的均方误差和Pearson相关系数来评估该方法。评估结果表明，当从同一细胞类型（即GM12878）中提取训练和测试数据时，以及从同一或不同物种的两种不同细胞类型（即GM12878作为训练与K562作为测试，GM12878作为训练与CH12-LX作为测试）中提取数据时，HiCNN始终优于文献中唯一现有的工具HiCPlus。我们进一步发现，与hicplus增强的数据相比，hicnn增强的高分辨率Hi-C数据与真实实验高分辨率Hi-C数据更一致，表明统计上显著的相互作用。此外，HiCNN可以有效地增强低分辨率的Hi-C数据，最终有助于恢复3D-FISH确认的两个染色质环。
Motivation: High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5kb) by conducting Hi-C experiments needs millions of mammalian cells, which may eventually generate billions of paired-end reads with a high sequencing cost. Therefore, it will be important and helpful if we can enhance the resolutions of Hi-C data by computational methods.Results: We developed a new computational method named HiCNN that used a 54-layer very deep convolutional neural network to enhance the resolutions of Hi-C data. The network contains both global and local residual learning with multiple speedup techniques included resulting in fast convergence. We used mean squared errors and Pearson's correlation coefficients between real high-resolution and computationally predicted high-resolution Hi-C data to evaluate the method. The evaluation results show that HiCNN consistently outperforms HiCPlus, the only existing tool in the literature, when training and testing data are extracted from the same cell type (i.e. GM12878) and from two different cell types in the same or different species (i.e. GM12878 as training with K562 as testing, and GM12878 as training with CH12-LX as testing). We further found that the HiCNN-enhanced high-resolution Hi-C data are more consistent with real experimental high-resolution Hi-C data than HiCPlus-enhanced data in terms of indicating statistically significant interactions. Moreover, HiCNN can efficiently enhance low-resolution Hi-C data, which eventually helps recover two chromatin loops that were confirmed by 3D-FISH.