Example data for chromatin interaction predictions using deepC.

This archive contains example files, data and models to run deepC predictions of chromatin interactions from DNA sequence, specifically the tutorials and example commands described in the deepC repository. https://github.com/rschwess/deepC<br> https://github.com/rschwess/deepHaem Copies of the deepC and deepHaem cover are included for continuity. Please see the deepC publication for details:<br> Schwessinger, R., Gosden, M., Downes, D. et al. DeepC: predicting 3D genome folding using megabase-scale transfer learning. Nat Methods 17, 1118–1124 (2020). https://doi.org/10.1038/s41592-020-0960-3 Human Hi-C is based on Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014). <br> Mouse Hi-C is based on Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572.e24 (2017). <br> DNase-seq and CTCF ChIP-seq were retrieved from the ENCODE Data portal (https://www.encodeproject.org/) Bernstein, B. E. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).本归档文件包含用于运行基于DNA序列的染色质相互作用deepC预测的示例文件、数据与模型，具体对应deepC仓库中介绍的教程与示例命令。相关仓库地址：https://github.com/rschwess/deepC；https://github.com/rschwess/deepHaem。为保证内容连贯性，本归档中附带deepC与deepHaem的相关说明副本。 如需了解详细信息，请参阅deepC的相关发表论文：Schwessinger, R., Gosden, M., Downes, D. 等. DeepC: 基于兆碱基级迁移学习预测三维基因组折叠. 自然方法 (Nat Methods) 17, 1118–1124 (2020). https://doi.org/10.1038/s41592-020-0960-3 人类Hi-C数据集基于Rao, S. S. P. 等2014年发表于《细胞》（Cell）的研究成果：《千碱基分辨率下的人类基因组三维图谱揭示染色质环化原理》，原文刊载于Cell 159, 1665–1680 (2014). 小鼠Hi-C数据集基于Bonev, B. 等2017年发表于《细胞》（Cell）的研究成果：《小鼠神经发育过程中的多尺度三维基因组重编程》，原文刊载于Cell 171, 557–572.e24 (2017). DNase测序（DNase-seq）与CTCF染色质免疫沉淀测序（ChIP-seq）数据均检索自ENCODE数据门户（https://www.encodeproject.org/），相关背景文献为：Bernstein, B. E. 等. 《人类基因组DNA元件整合百科全书》，原文刊载于《自然》（Nature）489, 57–74 (2012).