Encoding genomic architecture in the encyclopedia: linking DNA elements, chromatin state, and gene expression in 3D

自提交母校 U01 拨款以来，本节未曾更改。 项目概要 ENCODE 生成的数千个测序实验中的大多数都提供表观遗传的一维读数 3D 基因组的景观或转录输出。新染色体构象捕获 (3C) 技术——特别是 Hi-C 和 ChIA-PET——已经开始提供对分层 3D 的洞察 基因组的组织：将染色体划分为开放和封闭的区室；存在 定义为拓扑相关域（TAD）的结构亚基；以及监管和监管的存在 TAD 内的结构 DNA 环。使用 CRISPR/Cas 介导的基因组编辑的新实验证据 表明局部 3D 结构的破坏可以改变邻近基因的调节，并且已经有 早期尝试使用 3D DNA 循环数据来预测 GWAS 研究中非编码 SNP 的影响。 该提案的目标是开发新的综合计算方法来解释大规模 根据底层 3D 架构对 1D 表观基因组和转录组资源进行编码 基因组。我们团队的成员开创了从一维推断局部染色质状态的强大方法 透过赛格威套房的视角。我们还分析了染色质可及的一维组织 元素及其谱系动态来定义调控复杂性的概念，我们提出了一个基因 从活性 DNA 含量预测分化过程中基因表达变化的调控模型 增强剂。在这里，我们将在这些努力的基础上学习染色质状态和基因调控模型， 整合有关分层 3D 基因组架构的信息，使我们能够预测个体如何 结构/调控元件有助于 3D DNA 循环和基因表达。机理预测 将使用最先进的技术在其天然细胞类型特定染色质环境中进行实验验证 基因组编辑，利用我们团队开发的计算和实验 CRISPR/Cas 工具。

This section has not changed since the submission of the parent U01 grant. Project Summary Most of the 1000s of sequencing experiments generated by ENCODE provide 1D readouts of the epigenetic landscape or transcriptional output of a 3D genome. New chromosome conformation capture (3C) technologies – in particular Hi-C and ChIA-PET – have begun to provide insight into the hierarchical 3D organization of the genome: the partition of chromosomes into open and closed compartments; the existence of structural subunits defined as topologically associated domains (TADs); and the presence of regulatory and structural DNA loops within TADs. New experimental evidence using CRISPR/Cas-mediated genome editing suggests that disruption of local 3D structure can alter regulation of neighboring genes, and there have been early efforts to use data on 3D DNA looping to predict the impact of non-coding SNPs from GWAS studies. The goal of this proposal is to develop new integrative computational methods to interpret large-scale ENCODE 1D epigenomic and transcriptomic resources in light of the underlying 3D architecture of the genome. Members of our team have pioneered powerful methods to infer local chromatin states from a 1D viewpoint through the Segway suite. We have also analyzed the 1D organization of chromatin accessible elements and their lineage dynamics to define the concept of regulatory complexity, and we presented a gene regulation model to predict gene expression changes in differentiation from the DNA content of active enhancers. Here we will build on these efforts to learn chromatin state and gene regulation models that incorporate information on hierarchical 3D genomic architecture, enabling us to predict how individual structural/regulatory elements contribute to 3D DNA looping and to gene expression. Mechanistic predictions will be experimentally validated in their native cell-type specific chromatin context using state-of-the-art genome editing, exploiting computational and experimental CRISPR/Cas tools developed by our team.