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: 8739540
• 负责人: Carleton Lee Kingsford
• 金额: $ 44.1万
• 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/8739540
• 关键词: Accounting; Address; Affect; Algorithm Design; Algorithmic Software; Algorithms; Area; Biological Assay; Cells; Chromatin; Chromatin Structure; Chromosome Structures; Chromosomes; Collection; Computational Technique; Computer Analysis; 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）：我们提出研究用于与染色体构象捕获（3C）测定和相关实验相关的若干挑战的改进算法。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，如共表达基因，调控区域及其调控基因，共同断点位置等。然而，用于结构建模、将功能与结构相关联以及可视化3C数据的计算工具仍然缺乏。该提案旨在为几个中心3C分析任务开发计算工具。 在目标1中，我们提出了耦合采样与优化框架，以模拟与3C数据一致的染色质结构群体。这是必不可少的，因为3C提供了数百万个单元中不同结构的平均值。在目标2中，我们设计了在这些集合中找到共同和不同结构特征的技术，比较不同细胞类型的结构（例如癌症与正常;淋巴母细胞与成纤维细胞）和更好的技术来鉴定在统计学上显著空间共定位的基因组基因座。最后，在目标3中，我们提出开发一个“空间基因组浏览器”，它集成了一维基因组注释（基因、甲基化、DNA酶可及性等）3C空间数据 我们将应用这些技术来量化人类、酵母和小鼠中细胞间和细胞类型变异的数量。使用改进的人口模型，我们将确定新的情况下，远程调控和解释现有的假设远端增强子-启动子相互作用。我们还将结构与eQTL和GWAS鉴定的SNP相关联，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和空间邻近之间的关系。 我们提出的技术将导致更好的结构模型计算更有效，更好地理解结构和功能之间的关系。