Data Analysis and Modeling

数据分析和建模

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要/摘要映射基因组范围染色质结构的技术正在生成大量数据基因组的3D组织。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。主要挑战包括如何使用包含的静态信息在这些数据集中，在体内推断动态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，我们建议开发各种小说用于处理各种染色质拓扑数据集的分析方法，提取生物物理特性染色质纤维，并对染色质结构有深入的了解。在AIM 1中，我们将开发一种使用隐藏Markov随机字段的新统计方法来识别从全基因组染色质相互作用图。在AIM 2中，我们将开发用于分析数据的分析方法从基因组结构映射（GAM）实验，这是一种新型实验方法，我们将开发并将其作为映射组件的一部分。我们将进一步开发统计框架以重建3D 来自染色质触点和GAM数据集的染色质结构模型。在AIM 3中，我们将发展来自非平衡统计力学和聚合物物理学的预测模型，这些模型将连接染色质活细胞中的动力学到静态分子相互作用图。这些分析方法将共同提供染色质结构组织和动态特性的综合视图。