Reconstruction of 3D Genome Architecture from Chromatin Conformation Capture Data

从染色质构象捕获数据重建 3D 基因组架构

• 批准号: 8725712
• 负责人: MARK R SEGAL
• 金额: $ 37.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8725712
• 关键词: Accounting; Address; Agreement; Algorithms; Architecture; Automobile Driving; Biological; Biological Assay; Biological Process; Cell physiology; Characteristics; Chromatin; Chromosome abnormality; Data; Development; Elements; Evaluation; Gene Expression Regulation; Gene Fusion; Genome; Human; Malignant Neoplasms; Metric; Molecular; Molecular Conformation; Morbidity - disease rate; Nature; Nuclear; Paper; Performance; Play; Positioning Attribute; Publishing; Reproducibility; Resolution; Role; Series; Structure; Techniques; Testing; Work; insight; novel; public health relevance; reconstruction; three dimensional structure; tool

DESCRIPTION (provided by applicant): It is widely recognized that the three dimensional (3D) architecture of eukaryotic chromatin plays critical roles in nuclear and cellular function. Indeed, such relationships constitute one of the key "threads" of the just published (09/06/12) suite of Nature papers from the ENCODE consortium. However, until a few years ago, observing / inferring, 3D structure at even modest resolutions was problematic, in part because genomes are highly condensed. Recently devised molecular techniques are changing this situation. In particular, the development of novel genome-scale assays, such as chromatin conformation capture (CCC), has enabled elicitation of chromatin "contacts". These techniques have already provided insight into chromatin organization at unprecedented resolutions, and permitted exploration of the downstream influence of such organization on a variety of biological. Notably, gene regulation and cancer-driving gene fusions are believed to be strongly influenced by 3D organization. Accordingly, obtaining high resolution 3D reconstructions of genome architecture is a compelling biological quest. However, most analysis of CCC data has focused on the one dimensional (1D) contact level, with appreciably less effort directed toward evaluating accuracy and reproducibility of 3D reconstructions, and deploying such structures to analyze consequent biological processes. The overarching hypothesis to be addressed in this proposal is that chromatin contact data can be reliably used to determine 3D structures of genomes and to assess downstream relationships with biological function. To test this hypothesis we will develop new, and refine existing, reconstruction algorithms, and will undertake a systematic evaluation of their performance and operating characteristics. The only genome-scale algorithms developed employ constrained optimization which, on account of high-dimensionality, is computationally burdensome and can be trapped in local optima. Accordingly, we will investigate alternate algorithms. Our preliminary work indicates that reproducibility under plausible perturbations to data and constraint inputs is qualitatively poor. We will devise metrics to quantify 3D agreement. While reproducibility under perturbations can be assessed using computational and statistical tools, appraising accuracy requires using targeted confirmatory assays, as will be deployed by our wet-lab collaborators. Further, we will apply our compendium of 3D reconstructions to explore a series of biologic questions relating to proximities of functional elements.

描述（由申请人提供）：被广泛认识到真核染色质的三维（3D）结构在核和细胞功能中起关键作用。的确， 这种关系构成了来自编码财团的自然论文的刚刚发布（09/06/12）的关键“线程”之一。但是，直到几年前，观察 /推断，即使是适度分辨率的3D结构都存在问题，部分原因是基因组高度凝结。最近设计的分子技术正在改变这种情况。特别是，新型基因组规模测定法的发展，例如染色质构象捕获（CCC），已使染色质“接触”启发。这些技术已经在前所未有的决议中提供了对染色质组织的见解，并允许探索该组织对各种生物学的下游影响。值得注意的是，据信基因调节和癌症驱动基因融合受到3D组织的强烈影响。因此，获得基因组结构的高分辨率3D重建是一个引人注目的生物学任务。但是，大多数对CCC数据的分析都集中在一维（1D）的接触水平上，而努力减少了用于评估3D重建的准确性和可重复性的努力，并部署了此类结构来分析随后的生物学过程。本提案中要解决的总体假设是，可以可靠地使用染色质接触数据来确定基因组的3D结构并评估与生物学功能的下游关系。为了检验这一假设，我们将开发新的，并完善现有的重建算法，并将对其性能和操作特征进行系统评估。开发的唯一基因组规模算法使用受限的优化，由于高维度，它在计算上是繁重的，可以被困在局部Optima中。因此，我们将研究替代算法。我们的初步工作表明，在合理的数据和约束输入的合理扰动下，可重复性在质量上很差。我们将设计指标 量化3D协议。虽然可以使用计算和统计工具来评估扰动下的可重复性，但是评估准确性需要使用有针对性的确认性测定，这将由我们的湿与LAB合作者部署。此外，我们将应用3D重建的汇编来探讨与功能元素接近性有关的一系列生物学问题。