Breaking the Histone Code: Predicting Genome Organization with Chromatin States

打破组蛋白密码：用染色质状态预测基因组组织

• 批准号: 1715859
• 负责人: Bin Zhang
• 金额: $ 65.1万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715859&HistoricalAwards=false
• 关键词: Breaking; Histone; Code; Predicting; Genome

The human genome, composed of 46 DNA molecules (chromosomes), stretches for nearly two meters when fully extended, yet must fit into a cellular nucleus that is only 10 micrometers in diameter. A major unsolved question is how the genome is packaged and organized in this tiny three-dimensional space. This project seeks to address this question by developing novel theoretical and computational approaches to characterize the three-dimensional organization of the human genome. A Google Map-like resource for the genome will be designed such that one can easily navigate through its hierarchical organization across multiple length scales. This will prove an invaluable aid for studying genetic mechanisms related to the translation of information in the DNA genome to the RNA and protein molecules that constitute the "workhorses" of the cell. The project will also provide excellent interdisciplinary training that bridges chemistry, physics and biology for high school students, undergraduates, graduate students and postdoctoral fellows.This project aims to build a predictive genome model with an integrative framework that combines statistical mechanics and computational modeling with bioinformatics analysis. Novel theoretical approaches will be developed to derive the two key components of the model, including (1) an input sequence that uniquely represents a chromosome from a given cell type; and (2) a force field that describes the potential energy surface whose global minimum determines the most stable genome structure. To capture the variation of genome conformation across cell types, epigenetic information--including histone modifications, which are reflective of "chromatin states"--will be superimposed on top of the DNA sequence and used as input. Physical attributes will be used to maximize the information entropy. Long-range contact potentials will be derived from existing, publicly available genome-wide chromosome conformation capture data using a rigorous statistical optimization algorithm. This predictive genome model will not only enable a high-resolution characterization of the genome organization across a wide range of cell types, but can also help uncover the underlying physical principles and driving forces for genome folding. Moreover, the model will add value to the foundational biological datasets (from the human ENCODE project) and enable formulation of testable hypotheses relating genome organization to functional outputThis award is co-funded by the Genetic Mechanisms Program in the Division of Molecular and Cellular Biosciences in the Biological Sciences Directorate and by the Program for Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences in the Division of Mathematical Sciences in the Mathematical and Physical Sciences Directorate.

由46个DNA分子（染色体）组成的人类基因组在完全延伸时延伸了近2米，但必须适合直径仅10微米的细胞核。 一个未解决的主要问题是如何在这个微小的三维空间中包装和组织基因组。该项目旨在通过开发新颖的理论和计算方法来表征人类基因组的三维组织来解决这个问题。设计基因组的Google地图式资源，可以使人们可以在多个长度尺度上轻松浏览其层次结构组织。这将证明与研究与DNA基因组中的信息转换为构成细胞“工作马”的RNA和蛋白质分子有关的遗传机制的宝贵援助。该项目还将提供出色的跨学科培训，为高中生，本科生，研究生和博士后研究员提供化学，物理和生物学，该项目旨在通过综合框架建立一个预测的基因组模型，该模型将统计学机制和计算机制与生物信息信息分析结合在一起。将开发新的理论方法来得出模型的两个关键组成部分，包括（1）一个独特地代表给定细胞类型的染色体的输入序列； （2）描述势能表面的力场，其全局最小值决定了最稳定的基因组结构。为了捕获跨细胞类型的基因组构象的变化，表观遗传信息（包括反映“染色质状态”的组蛋白修饰）的表观遗传信息将叠加在DNA序列的顶部并用作输入。物理属性将用于最大化信息熵。远程接触电势将来自现有的，公开可用的全基因组染色体构象，使用严格的统计优化算法捕获数据。这种预测性基因组模型不仅将在广泛的细胞类型中对基因组组织进行高分辨率表征，而且还可以帮助发现基因组折叠的潜在物理原理和驱动力。此外，该模型将为基础生物学数据集（来自人类编码项目）增加价值，并能够提出可测试的假设，将基因组组织与功能输出相关的可测试假设与分子和细胞生物学家的遗传机制计划中的遗传机制计划共同资助，该计划是在生物科学和统计的计算和数据统计中，用于计算和数据统计的计算和数据统计，该奖项的数字科学和工程学分别是计算上的机构，该机构是数于计算和数据的机构。数学和物理科学局的科学。

项目成果

Predicting three-dimensional genome organization with chromatin states

• DOI: 10.1371/journal.pcbi.1007024
• 发表时间: 2019-06-01
• 期刊: PLOS COMPUTATIONAL BIOLOGY
• 影响因子: 4.3
• 作者: Qi, Yifeng;Zhang, Bin
• 通讯作者: Zhang, Bin

3D ATAC-PALM: super-resolution imaging of the accessible genome

• DOI: 10.1038/s41592-020-0775-2
• 发表时间: 2020-03-16
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Xie, Liangqi;Dong, Peng;Liu, Zhe
• 通讯作者: Liu, Zhe

Data-Driven Polymer Model for Mechanistic Exploration of Diploid Genome Organization

• DOI: 10.1016/j.bpj.2020.09.009
• 发表时间: 2020-11-03
• 期刊: BIOPHYSICAL JOURNAL
• 影响因子: 3.4
• 作者: Qi, Yifeng;Reyes, Alejandro;Zhang, Bin
• 通讯作者: Zhang, Bin

Learning the Formation Mechanism of Domain-Level Chromatin States with Epigenomics Data

• DOI: 10.1016/j.bpj.2019.04.006
• 发表时间: 2019-05-21
• 期刊: BIOPHYSICAL JOURNAL
• 影响因子: 3.4
• 作者: Xie，Wen Jun;Zhang，Bin
• 通讯作者: Zhang，Bin