Data Analysis and Modeling

数据分析和建模

• 批准号: 9021224
• 负责人: Bing Ren
• 金额: $ 42.67万
• 依托单位: LUDWIG INSTITUTE FOR CANCER RES LTD
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9021224
• 关键词: Ablation; Address; Adopted; Algorithms; Architecture; B-Lymphocytes; Bayesian Modeling; Binding; Bioinformatics; Biological Assay; Cell Nucleus; Cells; Chromatin; Chromatin Fiber; Chromatin Modeling; Chromatin Structure; Complex; Computing Methodologies; DNA; Data; Data Analyses; Data Set; Dependency; Development; Equation; Failure; Genetic; Genome; Genomic Segment; Gold; Imagery; Indium; Lead; Life; Ligation; Link; Maps; Mechanics; Methodology; Methods; Modeling; Motion; Nature; Pattern; Performance; Physics; Polymers; Process; Property; Publishing; Research; Series; Statistical Mechanics; Statistical Methods; Statistical Models; Structural Models; Technology; analytical method; base; biophysical properties; chromatin protein; computerized tools; data modeling; data structure; genome-wide; improved; in vivo; novel; predictive modeling; research study; simulation; spatial relationship; spatiotemporal; technology development; three-dimensional modeling; tool

PROJECT SUMMARY/ABSTRACT Technologies for mapping genome wide chromatin structure are generating tremendous amounts of data on the 3D organization of genomes. The size of these datasets and the unique data structure necessitate development of new data analysis tools. Major challenges include how to use the static information contained in these datasets to infer dynamic 3D chromatin organization in vivo, identify chromatin interactions, and gain an understanding of spatiotemporal chromatin organization. Here, we propose to develop a variety of novel analytical methodologies for processing various chromatin topology datasets, extracting biophysical properties of chromatin fibers, and gain an in-depth understanding of the chromatin architecture. In aim 1, we will development a new statistical method using hidden Markov random fields to identify chromatin contacts from the genome-wide chromatin interaction maps. In aim 2, we will develop analytical methods for analyzing data from Genome Architecture Mapping (GAM) experiment, a novel experimental methodology that we will develop and refine as a part of the mapping component. We will further develop statistical framework to reconstruct 3D chromatin structural models from both chromatin contacts and GAM datasets. In aim 3, we will develop predictive models from non-equilibrium statistical mechanics and polymer physics that will link chromatin dynamics in live cells to the static molecular interactions maps. Together, these analytical methods will provide comprehensive view of chromatin structural organization and dynamic properties.

项目总结/摘要 用于绘制全基因组染色质结构的技术正在产生大量的数据， 基因组的三维结构这些数据集的大小和独特的数据结构需要 开发新的数据分析工具。主要的挑战包括如何使用包含的静态信息 在这些数据集中推断体内动态3D染色质组织，识别染色质相互作用，并获得 对时空染色质组织的理解。在这里，我们建议开发各种新颖的 用于处理各种染色质拓扑数据集、提取生物物理特性 染色质纤维，并获得深入了解染色质架构。在目标1中， 开发一种新的统计方法，使用隐马尔可夫随机场识别染色质接触， 全基因组染色质相互作用图谱。在aim 2中，我们将开发分析数据的分析方法 从基因组结构映射（GAM）实验，一种新的实验方法，我们将开发 并作为映射组件的一部分进行细化。我们将进一步发展统计框架，以重建3D 来自染色质接触和GAM数据集的染色质结构模型。在目标3中，我们将开发 非平衡统计力学和聚合物物理学的预测模型， 从活细胞中的动态到静态分子相互作用图。这些分析方法将提供 全面了解染色质结构组织和动态特性。