Joint analysis of 3D chromatin organization and 1D epigenome

3D 染色质组织和 1D 表观基因组联合分析

• 批准号: 10441601
• 负责人: Jie Liu
• 金额: $ 42.53万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10441601
• 关键词: 3-Dimensional; Base Pairing; Cell Line; Cell Nucleus; ChIP-seq; Chromatin; Computer Models; Computing Methodologies; DNA Sequence; Data; Data Set; Dimensions; Disease; Elements; Epigenetic Process; Genetic; Genetic Variation; Genome; Genomics; Goals; Hi-C; Human Genome Project; Joints; Maps; Measures; Methods; Nucleosomes; Regulatory Element; Resolution; Signal Transduction; Three-dimensional analysis; Transcriptional Regulation; Untranslated RNA; Work; base; cell type; deep learning; epigenome; epigenomics; experimental study; genetic variant; genome annotation; genome wide association study; human disease; novel; transcription factor

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.

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描述了基因组沿着的调控元件和表观遗传景观。 最近，4D Nucleome（4DN）项目已经生成了超过2，000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件如何在空间上 组织成一个核心。3D染色质组织与先前描述的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， 长的基因组距离。然而，有两个挑战。首先，在以下两种情况之间存在分辨率不匹配： 染色质组织数据（例如Hi-C接触），通常以10 k碱基对分辨率测量，以及 基于表观基因组的染色质状态特征（例如ChIP-seq峰），其信号通常在数十到数百个 碱基对第二，现有的用于分析表观基因组的计算方法，例如注释基因组和 理解调控元件，都把DNA序列作为一维数据，留下重要的 未使用的3D结构信息。我们的目标是开发最前沿的深度学习方法， 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 基因组注释和鉴定空间协同转录因子。完成后 通过以上工作，我们希望：（1）建立一个准确的、可解释的染色质预测模型 广泛的细胞系的核小体分辨率的接触图，（2）3D和4D基因组注释， 动态染色质组织、调控元件和表观基因组特征，以及（3）用于 识别空间协作转录因子，这可以帮助我们理解非编码的编排， 基因变异这些结果将提供基本的了解疾病相关的遗传变异，在 鉴于这些基因组和表观基因组元件的空间组织及其功能意义。