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

自提交U 01母补助金以来，这一部分没有改变。 项目摘要 由ENCODE生成的1000多个测序实验中的大多数提供了表观遗传的1D读数。 3D基因组的横向或转录输出。新的染色体构象捕获（3C） 技术-特别是Hi-C和ChIA-PET -已经开始提供对分层3D的深入了解 基因组的组织：染色体分成开放和封闭的区室; 定义为拓扑相关结构域（TADs）的结构亚基;以及调节和 TADs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 使用3D DNA循环数据来预测GWAS研究中非编码SNP的影响的早期努力。 该提案的目标是开发新的综合计算方法来解释大规模的 ENCODE 1D表观基因组和转录组学资源，基于 基因组我们团队的成员开创了强大的方法来推断局部染色质状态从一维 通过赛格威套件的观点。我们还分析了染色质的1D组织， 元素及其谱系动力学来定义调控复杂性的概念，我们提出了一个基因 调控模型，以预测基因表达的变化，在分化的DNA含量的活性 增强剂。在这里，我们将建立在这些努力，学习染色质状态和基因调控模型， 结合分层3D基因组结构的信息，使我们能够预测个体 结构/调控元件有助于3D DNA成环和基因表达。机械预测 将使用最先进的技术在其天然细胞类型特异性染色质背景下进行实验验证 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.