hicGAN infers super resolution Hi-C data with generative adversarial networks
hicGAN infers super resolution Hi-C data with generative adversarial networks
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hicGAN 通过生成对抗网络推断超分辨率 Hi-C 数据
DOI：
10.1093/bioinformatics/btz317
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发表时间：
2019-07-15
期刊：
影响因子：
5.8
通讯作者：
Jiang, Rui
中科院分区：
文献类型：
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作者：
Liu, Qiao;Lv, Hairong;Jiang, Rui
Hi-C是一种全基因组范围的技术，通过测量基因组区域之间的物理接触来研究3D染色质构象。Hi-C数据的分辨率直接影响下游分析的有效性和准确性，例如识别拓扑相关结构域（TADs）和有意义的染色质环。高分辨率的Hi-C数据是研究基因组三维构象与功能之间关系的宝贵资源，特别是将远端调控元件连接到其靶基因。然而，由于高测序成本，跨各种组织和细胞类型的高分辨率Hi-C数据并不总是可用的。因此，它是必不可少的，以提高高碳数据的分辨率的计算方法。结果我们提出了hicGAN，一个开源框架，用于通过生成对抗网络（GAN）从低分辨率Hi-C数据推断高分辨率Hi-C数据。据我们所知，这是第一项将GAN应用于3D基因组分析的研究。我们证明，hicGAN通过生成与原始高分辨率Hi-C矩阵高度一致的矩阵，有效地提高了低分辨率Hi-C数据的分辨率。我们的方法的典型使用场景是增强新细胞类型中的低分辨率Hi-C数据，特别是在高分辨率Hi-C数据不可用的情况下。我们的研究不仅为提高Hi-C数据分辨率提供了一种新的方法，而且为揭示染色质接触形成的复杂机制提供了有趣的见解。可用性和实施我们在https://github.com/kimmo1019/hicGAN上将hicGAN作为开源软件发布。补充信息补充数据可在Bioinformatics在线获得。
Abstract Motivation Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating domains (TADs) and meaningful chromatin loops. High resolution Hi-C data are valuable resources which implicate the relationship between 3D genome conformation and function, especially linking distal regulatory elements to their target genes. However, high resolution Hi-C data across various tissues and cell types are not always available due to the high sequencing cost. It is therefore indispensable to develop computational approaches for enhancing the resolution of Hi-C data. Results We proposed hicGAN, an open-sourced framework, for inferring high resolution Hi-C data from low resolution Hi-C data with generative adversarial networks (GANs). To the best of our knowledge, this is the first study to apply GANs to 3D genome analysis. We demonstrate that hicGAN effectively enhances the resolution of low resolution Hi-C data by generating matrices that are highly consistent with the original high resolution Hi-C matrices. A typical scenario of usage for our approach is to enhance low resolution Hi-C data in new cell types, especially where the high resolution Hi-C data are not available. Our study not only presents a novel approach for enhancing Hi-C data resolution, but also provides fascinating insights into disclosing complex mechanism underlying the formation of chromatin contacts. Availability and implementation We release hicGAN as an open-sourced software at https://github.com/kimmo1019/hicGAN. Supplementary information Supplementary data are available at Bioinformatics online.