Statistical Methods For Annotating Repetitive Genomic Regions Through ENCODE-deri

通过 ENCODE-deri 注释重复基因组区域的统计方法

• 批准号: 8687990
• 负责人: Emery H Bresnick
• 金额: $ 41.55万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-17 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8687990
• 关键词: Address; Animal Model; Basic Science; Beryllium; Binding; Binding Sites; Biological; Biological Process; Biology; Biomedical Research; Blood Vessels; Cataloging; Catalogs; Cells; ChIP-seq; Chromatin; Clinical Research; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Deoxyribonucleases; Development; Diffuse; Disease; Drug Formulations; Elements; Enhancers; Evolution; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genome; Genomics; Goals; Hematopoiesis; Histones; Human; Human Genome; Indium; Joints; Location; Maps; Methods; Modeling; Molecular Biology; Mus; Nuclear; Pattern; Protein Isoforms; Reading; Regulatory Element; Repetitive Sequence; Role; Source; Specificity; Statistical Methods; Statistical Models; System; Technology; Testing; Tissues; Training; Validation; Work; base; cell type; computing resources; epigenomics; functional genomics; genome-wide; human GATA1 protein; improved; innovation; method development; next generation sequencing; programs; promoter; research study; response; tool development; transcription factor; transcriptome sequencing

DESCRIPTION (provided by applicant): The ENCODE projects have generated a wealth of high-quality genomic datasets with the applications of high- throughput next generation sequencing (NGS) to create a catalog of functional elements in the human and model organism genomes. Although the NGS technologies, embraced by ENCODE, are enabling interrogation of genomes in an unbiased manner, the data analysis efforts of the ENCODE projects have thus far focused on mappable regions of the genomes and thereby have not fully leveraged these data to their full advantage. A major bottleneck to a comprehensive understanding of data from the ENCODE projects is the lack of statistical and computational methods that can identify functional elements in repetitive regions. We will address this critical impediment in four specifi aims by building on our expertise in ChIP-seq and RNA-seq analysis. In Aim 1, we will develop probabilistic models and accompanying software for utilizing reads that map to multiple locations on the genome (multi-reads) from multiple types of *-seq datasets (ChIP-, DNase-, MeDIP-, and FAIRE-seq). This will enable cataloging of regulatory elements in repetitive regions. In Aim 2, we will improve the specificity of the discoveries in repetitive regions from ou probabilistic models by utilizing multiple related *- seq datasets simultaneously. Specifically, we will devise methods to supervise analysis of ChIP- and RNA-seq datasets by external ChIP-seq datasets. This will facilitate accurate inference for repetitive elements with near identical sequences, e.g., segmental duplications, long interspersed nuclear elements, and boost accuracy of gene and isoform quantification with RNA-seq. In Aim 3, we will focus on identifying co-occupied/enriched regions to infer cell-specific modules of regions/genes and their regulatory profiles. We will also develop a formal differential co-enrichment framework to study cell-specific wiring and interactions of regulatory factors. This will elucidate how interactions among regulatory factors vary across cells/tissues/conditions. Aim 4, we will apply our methods from Aims 1-3 to relevant ENCODE data to understand GATA factor functions in hematopoiesis and vascular biology. The GATA system in human and mouse will serve as a training and validation platform for our methods. Statistical and computational resources generated from the project, which will be disseminated as modular and robust software, will help to enhance and maximize the impact of ENCODE-derived data on the biomedical research community.

描述（由申请人提供）：ENCODE项目已经产生了大量高质量的基因组数据集，应用高通量下一代测序（NGS）创建了人类和模式生物基因组中功能元件的目录。尽管ENCODE所采用的NGS技术能够以公正的方式对基因组进行询问，但ENCODE项目的数据分析工作迄今为止一直集中在基因组的可映射区域，因此没有充分利用这些数据。全面了解ENCODE项目数据的一个主要瓶颈是缺乏可以识别重复区域中功能元件的统计和计算方法。我们将通过利用我们在ChIP-seq和RNA-seq分析方面的专业知识，在四个具体目标中解决这一关键障碍。在目标1中，我们将开发概率模型和配套软件，用于利用从多种类型的 *-seq数据集（ChIP-，DNase-，MeDIP-和FAIRE-seq）映射到基因组上多个位置的读段（多读段）。这将使得能够对重复区域中的调控元件进行编目。在目标2中，我们将通过同时利用多个相关的 *- seq数据集来提高概率模型中重复区域发现的特异性。我们特别 将设计方法来监督外部ChIP-seq数据集对ChIP和RNA-seq数据集的分析。这将有助于准确推断具有近似相同序列的重复元件，例如，片段重复，长散布的核元件，并提高基因和亚型定量的准确性与RNA-seq。在目标3中，我们将专注于识别共占据/富集区域，以推断区域/基因的细胞特异性模块及其调控谱。我们还将开发一个正式的差异共富集框架来研究细胞特异性 调节因子的连接和相互作用。这将阐明调节因子之间的相互作用如何在细胞/组织/条件下变化。目的4，我们将把我们的方法从目的1-3应用到相关的ENCODE数据中，以了解加塔因子在造血和血管生物学中的功能。人类和小鼠的加塔系统将作为我们方法的训练和验证平台。该项目产生的统计和计算资源将作为模块化和强大的软件传播，将有助于加强和最大限度地扩大ENCODE产生的数据对生物医学研究界的影响。