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）：人们普遍认为，真核染色质的三维（3D）结构在核和细胞功能中发挥着关键作用。的确， 这种关系构成了 ENCODE 联盟刚刚发表（2012 年 9 月 6 日）的《自然》系列论文的关键“线索”之一。然而，直到几年前，即使在中等分辨率下观察/推断 3D 结构也是有问题的，部分原因是基因组高度浓缩。最近发明的分子技术正在改变这种情况。特别是，新型基因组规模检测的发展，例如染色质构象捕获（CCC），已经能够引发染色质“接触”。这些技术已经以前所未有的分辨率提供了对染色质组织的深入了解，并允许探索这种组织对多种生物的下游影响。值得注意的是，基因调控和癌症驱动基因融合被认为受到 3D 组织的强烈影响。因此，获得基因组结构的高分辨率 3D 重建是一项引人注目的生物学探索。然而，大多数 CCC 数据分析都集中在一维 (1D) 接触水平，而很少致力于评估 3D 重建的准确性和可重复性，以及部署此类结构来分析后续的生物过程。该提案要解决的首要假设是染色质接触数据可以可靠地用于确定基因组的 3D 结构并评估下游与生物功能的关系。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，并对它们的性能和操作特性进行系统评估。开发的唯一基因组规模算法采用约束优化，由于高维性，计算量很大并且可能陷入局部最优。因此，我们将研究替代算法。我们的初步工作表明，在数据和约束输入的合理扰动下，再现性质量很差。我们将制定衡量标准 量化 3D 一致性。虽然可以使用计算和统计工具评估扰动下的再现性，但评估准确性需要使用有针对性的验证性分析，正如我们的湿实验室合作者将部署的那样。此外，我们将应用 3D 重建概要来探索一系列与功能元素邻近性相关的生物学问题。