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: 9060461
• 负责人: Emery H Bresnick
• 金额: $ 29.92万
• 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/9060461
• 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; DNase I hypersensitive sites sequencing; Data; Data Analyses; Data Set; Deoxyribonucleases; Development; Diffuse; Disease; Drug Formulations; Elements; Enhancers; Evolution; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genome; Genomic Segment; 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）：编码项目与高吞吐量下一代测序（NGS）的应用产生了丰富的高质量基因组数据集，以创建人类和模型生物体基因组中功能元素的目录。尽管由编码所接受的NGS技术正在以公正的方式审问基因组，但迄今为止，编码项目的数据分析工作集中在基因组的可映射区域上，因此并未完全利用这些数据以充分利用它们的优势。对来自编码项目数据的全面了解的主要瓶颈是缺乏统计和计算方法，可以识别重复区域中的功能元素。我们将通过在ChIP-Seq和RNA-Seq分析方面的专业知识来解决四个特定目标中的这一关键障碍。在AIM 1中，我们将开发概率模型和随附的软件，以利用从多种 *-Seq数据集（chip-，dnase-，medip-和faire-seq）的多种类型的基因组（多阅读）上映射到基因组（多阅读）的读取。这将使重复区域的监管要素分类。在AIM 2中，我们将通过同时利用多个相关的 *-SEQ数据集，从而提高重复区域中发现中发现的特异性。具体来说，我们 将设计方法来监督外部CHIP-seq数据集对芯片和RNA-seq数据集的分析。这将有助于对具有接近相同序列的重复元素进行准确的推论，例如，分段重复，散布的核元素，以及基因的增强精度和使用RNA-seq的同工型定量。在AIM 3中，我们将专注于确定相互占领/丰富的区域以推断区域/基因的细胞特异性模块及其调节曲线。我们还将开发一个形式的差分共同增强框架来研究细胞特异性 调节因素的接线和相互作用。这将阐明调节因素之间的相互作用如何在细胞/组织/条件之间变化。 AIM 4，我们将将我们的方法从AIMS 1-3应用于相关的编码数据，以了解造血和血管生物学中的GATA因子功能。人和鼠标中的GATA系统将作为我们方法的培训和验证平台。该项目产生的统计和计算资源将作为模块化和健壮的软件传播，将有助于增强和最大化编码数据对生物医学研究社区的影响。