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

使用 DeepLo​​op 从稀疏或单细胞 Hi-C 数据中稳健地绘制染色质环

• 批准号: 10676223
• 负责人: Fulai Jin
• 金额: $ 55.96万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-03 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676223
• 关键词: 3-Dimensional; Address; Affect; Benchmarking; Brain; Cell Nucleus; Cells; Chromatin; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Chromatin Loop; Chromosome Mapping; Communities; Complex; Copy Number Polymorphism; DNA Folding; DNA mapping; Data; Data Analyses; Data Set; Digestion; Disease; Error Sources; Financial Hardship; Future; Gene Expression; Genes; Genets; Genome; Genome Mappings; Goals; Grant; Hi-C; Hiccup; Homologous Gene; Human; Ice; Islets of Langerhans; Libraries; Maps; Methods; Names; Noise; Paper; Performance; Phase; Population; Protocols documentation; Publishing; Regulator Genes; Reporting; Research; Resolution; Resources; Sampling; Scientist; Signal Transduction; Structure; Systematic Bias; Techniques; Technology; Tissues; Variant; Work; computerized tools; cost; data resource; deep learning; deep sequencing; genome analysis; genome-wide; improved; innovation; mammalian genome; method development; performance tests; promoter; success; tool

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.

项目摘要 绘制拓扑相关域内基因调控染色质相互作用的图谱（子- TAD）仍然是 3D 基因组研究的主要挑战。人们普遍认为，阅读量达数十亿次 由于复杂的偏差结构，千碱基分辨率的 Hi-C 分析需要测序深度 以及严重的数据稀疏性。然而，我们最近发现这个问题可以在很大程度上得到解决 通过计算无需超深测序。我们开发了一个新的管道，名为 DeepLo​​op 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。这 DeepLo​​op的概念创新在于分别处理系统偏差和随机噪声：我们 使用 HiCorr 提高偏差校正的严格性，然后应用深度学习技术进行噪声 减少和环路信号增强。初步结果表明DeepLo​​op显着提高 Hi-C 环分析的灵敏度、稳健性和定量性，可用于重新分析大多数 发布了低深度 Hi-C 数据集。值得注意的是，DeepLo​​op 可以利用 Hi-C 数据识别染色质环 由几十个单细胞组成。这些成功激励我们进一步优化、基准测试、简化和 将 DeepLo​​op 升级为 3D 基因组领域的多功能工具。目标 1 将优化和基准测试 DeepLo​​op性能，提高其与多种不同Hi-C协议的兼容性，并扩展 它在超分辨率分析中的实用性。目标 2 将开发新的基于 DeepLo​​op 的管道，以实现稳健的 以高分辨率绘制动态染色质环，包括同源特异性的识别 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，用于高 使用单细胞 Hi-C 对复杂组织进行分辨率循环分析，将获得大量数据 该项目中产生的作为科学界的资源。