The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we have developed a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully imputed high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identified the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR's potential in coupling transcriptional regulation with 3D chromatin organization at high resolution.

中文翻译：

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将高分辨率3D染色质组织与表观基因组学联系起来

染色质构象捕获技术的分辨率不断提高，最近的核小体分辨率染色质接触图使我们能够探索精细3D染色质组织与人类细胞表观基因组状态之间的关系。使用公开可用的Micro-C数据集，我们开发了深度学习模型CAESAR，以学习从表观基因组特征到3D染色质组织的映射功能。该模型可以准确预测Hi-C无法检测到的精细结构，例如短距离染色质环和条纹。利用ENCODE和Roadmap Epigenomics Project的现有表观基因组数据集，我们成功估算了91种人体组织和细胞系的高分辨率3D染色质接触图。在估算的高分辨率联系人图中，