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: 10046394
• 负责人: Jie Liu
• 金额: $ 43.28万
• 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/10046394
• 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; 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; three dimensional structure; 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.

抽象的 人类基因组计划完成后，ENCODE 和 Roadmap 进行了数千次实验 表观基因组学项目已成功描绘了基因组的调控元件和表观遗传景观。 最近，4D 核组 (4DN) 项目生成了 2,000 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在空间上如何分布的补充信息 3D 染色质组织与先前分析的 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 然而，存在两个挑战：首先，两者之间存在分辨率不匹配。 通常以 10k 碱基对分辨率测量的染色质组织数据（例如 Hi-C 接触），以及 基于表观基因组的染色质状态特征（例如 ChIP-seq 峰），其信号通常为数十到数百 其次，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 了解调控元件，都将DNA序列视为一维数据，留下了重要的 未利用的 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 分别完成基因组注释和空间协同转录因子识别。 在所提出的工作中，我们期望：（1）一个准确且可解释的计算模型来预测染色质 多种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、调控元件和表观基因组特征，以及（3）计算方法 识别空间协作转录因子可以帮助我们理解非编码的编排 这些结果将为我们提供对疾病相关遗传变异的基本了解。 根据这些基因组和表观基因组元素的空间组织及其功能含义。