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.

人类基因组由46个DNA分子（染色体）组成，完全伸展时可延伸近两米，但必须装入直径仅为10微米的细胞核中。 一个尚未解决的主要问题是基因组如何在这个微小的三维空间中包装和组织。该项目旨在通过开发新的理论和计算方法来描述人类基因组的三维组织来解决这个问题。将设计一个类似谷歌地图的基因组资源，以便人们可以轻松地在多个长度尺度上浏览其层次结构。这将证明是一个宝贵的援助，研究遗传机制有关的翻译信息在DNA基因组中的RNA和蛋白质分子，构成了细胞的“主力”。该项目还将为高中生、本科生、研究生和博士后研究员提供优秀的跨学科培训，将化学、物理和生物学联系起来。该项目旨在建立一个预测基因组模型，该模型具有将统计力学和计算建模与生物信息学分析相结合的综合框架。将开发新的理论方法来推导模型的两个关键组成部分，包括（1）唯一代表给定细胞类型染色体的输入序列;（2）描述势能面的力场，其全局最小值决定最稳定的基因组结构。为了捕获不同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-将叠加在DNA序列之上并用作输入。物理属性将用于最大化信息熵。远程接触电位将使用严格的统计优化算法从现有的公开可用的全基因组染色体构象捕获数据中获得。这种预测性基因组模型不仅能够在广泛的细胞类型中对基因组组织进行高分辨率表征，而且还可以帮助揭示基因组折叠的潜在物理原理和驱动力。此外，委员会认为，该模型将为基础生物数据集增加价值，该奖项由生物科学理事会分子和细胞生物科学部的遗传机制计划和计算和数据计划共同资助，在数学和物理科学理事会数学科学司数学和统计科学方面启用科学和工程。

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