Joint analysis of 3D chromatin organization and 1D epigenome

摘要 人类基因组计划完成后，来自Encode和Roadmap的数千项实验 表观基因组学项目已经成功地支持fi领导的调控元件和沿基因组的表观遗传学图景。 最近，从4D核组(4DN)计划产生了2000多个染色质组织数据集， 它们提供了关于这些基因组和表观基因组元素如何在空间上的补充信息 以原子核组织起来的。三维染色质组织与前fi引导的1D表观基因组的联合分析 不同的细胞类型将是理解转录调控机制的关键一步。 基因组距离很远。然而，有两个挑战。首先，存在分辨率不匹配的问题 染色质组织数据(例如，Hi-C接触)，通常在10k碱基对分辨率下测量，以及 基于表观基因组的染色质状态特征(例如，芯片序列峰)，其信号通常在数十到数百 碱基对。第二，现有的表观基因组分析的计算方法，如注释基因组和 理解调控元件，都将DNA序列视为一维数据，留下重要的 未利用的三维结构信息。我们的目标是开发最尖端的深度学习方法 了解染色质状态特征和染色质组织之间的关系，执行3D和4D操作 基因组注释和识别空间协同转录因子。在完成后 提出的工作，我们期望有：(1)一个准确的和可解释的计算模型来预测染色质 广泛细胞系的核小体分辨率接触图，(2)3D和4D基因组注释 动态染色质组织、调节元件和表观基因组特征，以及(3)一种计算方法 识别空间协同转录因子，帮助我们理解非编码的编排 基因变异。这些结果将提供对与疾病相关的遗传变异的基本理解 这些基因组和表观基因组元素的空间组织及其功能含义。

Abstract After the completion of the Human Genome Project, thousands of experiments from ENCODE and Roadmap Epigenomics projects have successfully profiled regulatory elements and epigenetic landscape along the genome. More recently, over 2,000 chromatin organization datasets have been generated from 4D Nucleome (4DN) Project, and they provide complementary information about how these genomic and epigenomic elements are spatially organized in a nucleus. Joint analysis of 3D chromatin organization with previously profiled 1D epigenome in different cell types will be a key step to understand the mechanisms underlying transcriptional regulation over long genomic distances. However, there are two challenges. First, there is a resolution mismatch between chromatin organization data (e.g. Hi-C contacts) which are usually measured at 10k base pair resolution, and epigenome-based chromatin state features (e.g. ChIP-seq peaks) whose signals are usually at tens to hundreds of base pairs. Second, existing computational approaches for analyzing epigenome, such as annotating genome and understanding regulatory elements, all treat the DNA sequence as one-dimensional data, leaving the important 3D structural information unutilized. We aim to develop the most cutting-edge deep learning approaches for understanding the relationship between chromatin state features and chromatin organization, performing 3D and 4D genome annotation, and identifying spatially collaborative transcription factors, respectively. After the completion of the proposed work, we expect to have: (1) an accurate and interpretable computational model to predict chromatin contact maps at nucleosome resolution for a wide range of cell lines, (2) 3D and 4D genome annotations over dynamic chromatin organization, regulatory elements and epigenomic features, and (3) a computational method for identifying spatially collaborative transcription factors which can help us understand the orchestration of noncoding genetic variants. These results will provide fundamental understanding of disease-relevant genetic variation in the light of the spatial organization of these genomic and epigenomic elements and their functional implications.