Algorithms for Managing Uncertainty in Chromosome Conformation Capture Data

管理染色体构象捕获数据不确定性的算法

• 批准号: 8579049
• 负责人: Carleton Lee Kingsford
• 金额: $ 45万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-23 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8579049
• 关键词: Accounting; Address; Affect; Algorithms; Area; Biological Assay; Cells; Chromatin; Chromatin Structure; Chromosome Structures; Chromosomes; Collection; Computational Technique; Computer Analysis; Computer software; Coupled; Coupling; DNA biosynthesis; Data; Data Correlations; Development; Disease; Distal; Enhancers; Evolution; Fibroblasts; Foundations; Genes; Genome; Genomic Segment; Genomics; Graph; Human; Imagery; Internet; Location; Malignant Neoplasms; Measurement; Measures; Methods; Methylation; Mining; Modeling; Molecular Conformation; Mus; Nucleic Acid Regulatory Sequences; Organism; Pattern; Plug-in; Population; Population Control; Progeria; Regulation; Reporting; Research; Sampling; Scheme; Site; Skin; Software Tools; Structural Models; Structure; Techniques; Testing; Uncertainty; Validation; Variant; Yeasts; base; cancer cell; cancer type; cell type; computerized tools; crosslink; design; genome wide association study; improved; insight; novel; programs; promoter; public health relevance; research study; restriction enzyme; spatial relationship; task analysis; three-dimensional modeling; two-dimensional

DESCRIPTION (provided by applicant): We propose to investigate improved algorithms for several challenges associated with chromosome conformation capture (3C) assays and related experiments. These recent high-throughput experiments give pairwise contact information between chromatin regions and have provided glimpses of the spatial organization of the genomes of several organisms. They have been used to computationally infer three-dimensional models of chromatin structure and to hypothesize functional spatial relationships among genomic features such as co-expressed genes, regulatory regions and their regulated genes, common breakpoint locations, and others. However, computational tools for structural modeling, relating function to structure, and for visualizing 3C data are still lacking. This proposal seeks to develop computational tools for several central 3C analysis tasks. In Aim 1, we propose coupling sampling with an optimization framework to model populations of chromatin structures that are consistent with 3C data. This is essential because 3C provides an average over different structures in millions of cells. In Aim 2, we devise techniques to find common and different structural features within these ensembles, comparing structures of different cell types (e.g. cancer vs. normal; lyphoblastoid vs. fibroblast) and better techniques t identify genomic loci that are statistically significantly spatially co-located. Finally, in Aim 3 e propose to develop a "spatial genome browser" that integrates both 1-d genomic annotations (genes, methylation, DNAase accessibility, etc.) with 3C spatial data. We will apply these techniques to quantifying the amount of cell-to-cell and cell-type variation in human, yeast, and mouse. Using improved populations of models, we will identify new instances of long-range regulation and explain existing postulated distal enhancer-promoter interactions. We will also correlate structure with eQTLs and GWAS-identified SNPs to explain the mechanism causing the eQTL and the effect of the SNP. Finally, we will search for relationships between co-expressed genes and spatial proximity. The techniques we propose will result in better structural models computed more efficiently and a better understanding of the relationships between structure and function.

描述（由申请人提供）：我们建议研究改进的算法，以应对与染色体构象捕获（3C）测定和相关实验相关的几个挑战。这些最近的高通量实验提供了染色质区域之间的成对接触信息，并提供了几种生物体基因组空间组织的一瞥。它们已被用于计算推断染色质结构的三维模型，并假设基因组特征之间的功能空间关系，例如共表达基因、调控区域及其调控基因、常见断点位置等。然而，结构建模、功能与结构关联以及 3C 数据可视化的计算工具仍然缺乏。该提案旨在开发用于多个核心 3C 分析任务的计算工具。 在目标 1 中，我们建议将采样与优化框架结合起来，以对与 3C 数据一致的染色质结构群体进行建模。这是至关重要的，因为 3C 提供了数百万个细胞中不同结构的平均值。在目标 2 中，我们设计了一些技术来发现这些集合中的共同和不同的结构特征，比较不同细胞类型的结构（例如癌症与正常细胞；类淋巴母细胞与成纤维细胞）以及更好的技术来识别在统计上显着空间共存的基因组位点。最后，在目标 3 中，我们建议开发一个“空间基因组浏览器”，它将 1-d 基因组注释（基因、甲基化、DNAase 可访问性等）与 3C 空间数据集成在一起。 我们将应用这些技术来量化人类、酵母和小鼠的细胞间和细胞类型变异的数量。使用改进的模型群体，我们将识别远程调节的新实例，并解释现有假设的远端增强子-启动子相互作用。我们还将把结构与 eQTL 和 GWAS 识别的 SNP 关联起来，以解释引起 eQTL 的机制和 SNP 的作用。最后，我们将寻找共表达基因与空间邻近性之间的关系。 我们提出的技术将带来更好的结构模型，更有效地计算，并更好地理解结构和功能之间的关系。