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

基本信息

批准号：
10703360
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
项目类别：
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

项目摘要

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.

抽象的在人类基因组项目压缩之后，编码和路线图的数千个实验表观基因组学项目已成功实现最近，已经从4D核心（4DN）项目中生成了2,000多个染色质组织数据集，并且它们提供有关Thourse基因组和表观基因组学元素的编译信息是空间的在3D染色质组织的核中组织不同的单元类型将是了解转录基础机制的关键步骤但是，基因组距离。染色质组织数据（例如HI-C联系人）通常以10K基对分辨率测量，并且基于表观基因组的染色质状态特征（例如CHIP-SEQ峰），其信号通常在数十个到数百个时基本对。了解调节元素，都将DNA序列视为一维数据，使重要 3D结构信息未经利用。了解染色质状态特征与染色质组织之间的关系，执行3D和4D 4D 基因组注释，并识别在空间协作的转录因子的尊重者。支撑工作，我们希望拥有：（1）一种准确且可解释的计算模型来预测染色质核小体分辨率的接触图，用于广泛的细胞系，（2）3D和4D基因组注释动态染色质组织，调节元素和表观基因组特征，以及（3）一种用于的计算机方法识别空间协作的转录因素，可以帮助我们无编码这些结果将主要理解与疾病相关的遗传学这些基因组和表观基因组元素的空间组织以及功能含义的光。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Characterizing collaborative transcription regulation with a graph-based deep learning approach.

DOI：
10.1371/journal.pcbi.1010162
发表时间：
2022-06
期刊：
PLoS computational biology
影响因子：
4.3
作者：
通讯作者：

scHiCTools: A computational toolbox for analyzing single-cell Hi-C data.

DOI：
10.1371/journal.pcbi.1008978
发表时间：
2021-05
期刊：
PLoS computational biology
影响因子：
4.3
作者：
Li X;Feng F;Pu H;Leung WY;Liu J
通讯作者：
Liu J

GenomicKB: a knowledge graph for the human genome.

DOI：
10.1093/nar/gkac957
发表时间：
2023-01-06
期刊：
NUCLEIC ACIDS RESEARCH
影响因子：
14.9
作者：
Feng, Fan;Tang, Feitong;Gao, Yijia;Zhu, Dongyu;Li, Tianjun;Yang, Shuyuan;Yao, Yuan;Huang, Yuanhao;Liu, Jie
通讯作者：
Liu, Jie

3'HS1 CTCF binding site in human β-globin locus regulates fetal hemoglobin expression.

DOI：
10.7554/elife.70557
发表时间：
2021-09-29
期刊：
eLife
影响因子：
7.7
作者：
Himadewi P;Wang XQD;Feng F;Gore H;Liu Y;Yu L;Kurita R;Nakamura Y;Pfeifer GP;Liu J;Zhang X
通讯作者：
Zhang X

A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.