Comparative analysis and regulatory architecture of epigenomics datasets

 描述（由申请人提供）：NIH Roadmap表观基因组学和ENCODE项目已经生成了3000多个表观基因组学数据集，包括190种细胞和组织类型的组蛋白修饰、DNA甲基化、基因表达和DNaseI超敏反应。为了最大限度地发挥其对基因调控，细胞分化和人类健康的影响，需要新的计算分析。为了应对这一挑战，我们将开发新的表观基因组分析方法，建立在我们解释表观基因组信息的丰富经验，以及我们为Roadmap表观基因组学和ENCODE数据集构建染色质状态，活性簇和调控基序图的初步研究基础上。在目标1中，我们将通过开发新的工具来描述谱系分化过程中的表观基因组差异和变化，这些工具用于直接利用表观基因组数据集的复杂性的表观基因组的系统比较;我们还将开发基于自动学习的不同表观基因组特征将表观基因组聚类到发育谱系中的方法。以及学习多能细胞在谱系定型期间经历的单向表观基因组变化以获得对分化的更多了解并自动学习对谱系和分化轨迹进行分类的方法。在Am 2中，我们将寻求表征高阶染色质结构和染色质构象，以系统地解释顺式调控模块：我们将开发一种新的增强子-增强子和增强子-基因连接的统计方法，以揭示相互作用区域及其靶基因，基于它们在细胞和组织类型中的协调活性模式;我们将训练一种监督学习方法，用于基于染色质状态信息、个体染色质标记、基因组距离、活性、调控基序信息和DNA序列来预测组成性和组织特异性染色质构象信息;我们将使用这些高阶相互作用图谱来预测基于多个调控区域的联合作用的基因表达水平，并定义人类基因组中每个基因的顺式调控结构。由此产生的资源将是非常宝贵的基因调控的研究，揭示了一套调控元件，是连接到每个基因，并为遗传研究的解释，揭示了一套调控元件，共同作用， 调节每个靶基因和与人类疾病相关的非编码变体的潜在靶基因。

 DESCRIPTION (provided by applicant): The NIH Roadmap Epigenomics and ENCODE projects have generated a collection of 3000+ epigenomics datasets, including histone modification, DNA methylation, gene expression, and DNaseI hypersensitivity profiled across 190 cell and tissue types. In order to maximize its impact on gene regulation, cellular differentiation, and human health, novel computational analyses are needed. To address this challenge, we will develop new methods for epigenomic analysis, building on our extensive experience interpreting epigenomic information, and our preliminary studies building chromatin states, activity clusters, and regulatory motif maps for the Roadmap Epigenomics and ENCODE datasets. In Aim 1, we will characterize epigenomic differences and changes during lineage differentiation by developing new tools for systematic comparison of groups of epigenomes that directly exploit the complexity of epigenomic datasets; we will also develop methods for clustering epigenomes into developmental lineages based on automatically-learned diverse epigenomic features that distinguish them; and methods that learn the unidirectional epigenomic changes that pluripotent cells undergo during lineage commitment to gain more insights into differentiation and automatically learn to classify lineages and differentiation trajectories. In Am 2, we will seek to characterize higher-order chromatin architecture and chromatin conformation to enable systematic interpretation of cis-regulatory modules: we will develop a novel statistical approach for enhancer-enhancer and enhancer-gene linking to reveal interacting regions and their target genes based on their coordinated activity patterns across cell and tissue types; we will train a supervised learning method for predicting both constitutive and tissue-specific chromatin conformation information based on chromatin state information, individual chromatin marks, genomic distance, activity, regulatory motif information, and DNA sequence; and we will use these higher-order interaction maps to predict gene expression levels based on the combined action of multiple regulatory regions and to define the cis-regulatory architecture of each gene in the human genome. The resulting resources will be invaluable for studies of gene regulation, by revealing the set of regulatory elements that are linked to each gene, and for the interpretation of genetic studies, by revealing the set of regulatory elements which jointly act to regulate each target gene and the potential target genes of non-coding variants associated with human disease.