Robust mapping of chromatin loops from sparse or single cell Hi-C data with DeepLoop

项目摘要 在拓扑相关结构域（亚结构域）内绘制基因调控染色质相互作用的图谱， 3D基因组研究中的一个主要挑战。人们普遍认为， 由于复杂的偏倚结构，在内切酶分辨率下进行Hi-C分析需要测序深度 和严重的数据稀疏性。然而，我们最近发现，这个问题可以在很大程度上得到解决 无需超深度测序。我们开发了一种新的管道， DeepLoop可以从低深度Hi-C数据中稳健地识别高分辨率染色质相互作用。的 DeepLoop的概念创新是分别处理系统偏差和随机噪声：我们 使用HiCorr来提高偏差校正的精确性，然后应用深度学习技术来处理噪声 减少和环路信号增强。初步结果显示，DeepLoop显著改善了 Hi-C环分析的灵敏度、稳健性和定量，并可用于重新分析大多数 低深度Hi-C数据集。值得注意的是，DeepLoop可以通过Hi-C数据识别染色质环 从几十个单细胞。这些成功激励我们进一步优化、基准测试、简化和 将DeepLoop升级为3D基因组领域的通用工具。目标1将优化和基准 DeepLoop性能，提高其与各种不同Hi-C协议的兼容性，并扩展 它对超分辨率分析的效用。Aim 2将开发新的基于DeepLoop的管道， 高分辨率动态染色质环的映射，包括同源特异性的鉴定 循环和受结构变体影响的循环。Aim 3将开发一套完整的解决方案， 使用单细胞Hi-C进行复杂组织的分辨率环分析，将获得大量数据。 作为科学界的资源。

Project Abstract Mapping the gene-regulatory chromatin interactions within topologically associated domains (sub- TAD) remains a major challenge in 3D genome research. It is generally believed that multibillion-read sequencing depth are required for Hi-C analysis at kilobase-resolution due to the complex bias structure and severe data sparsity. However, we recently discovered that this is problem can be largely solved computationally without the need for ultradeep-sequencing. We developed a new pipeline named DeepLoop that can robustly identify high-resolution chromatin interactions from low-depth Hi-C data. The conceptual innovation of DeepLoop is to handle systematic biases and random noises separately: we used HiCorr to improve the rigor of bias correction, and then applied deep-learning techniques for noise reduction and loop signal enhancement. Preliminary results showed that DeepLoop significantly improves the sensitivity, robustness, and quantitation of Hi-C loop analyses, and can be used to reanalyze most published low-depth Hi-C datasets. Remarkably, DeepLoop can identify chromatin loops with Hi-C data from a few dozen single cells. These successes motivate us to further optimize, benchmark, simplify and upgrade DeepLoop into a versatile tool for the 3D genome field. Aim 1 will optimize and benchmark DeepLoop performance, improve its compatibility with a variety of different Hi-C protocols, and expand its utility to ultra-resolution analysis. Aim 2 will develop new DeepLoop-based pipelines to enable robust mapping of dynamic chromatin loops at high-resolution, including the identification of homolog-specific loops and loops affected by structure variants. Aim 3 will develop a full-package solution for high- resolution loop analysis of complex tissues with single cell Hi-C, a significant amount of data will be generated in this project as a resource for the scientific community.