Statistical Methods For Annotating Repetitive Genomic Regions Through ENCODE-deri

描述(由申请人提供)：ENCODE项目利用高通量下一代测序(NGS)应用程序生成了丰富的高质量基因组数据集，以创建人类和模型生物基因组中的功能元件目录。尽管ENCODE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侧重于基因组的可绘制区域，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能够确定重复区域中的功能要素的统计和计算方法。我们将通过在CHIP-SEQ和RNA-SEQ分析方面的专业知识，通过四个具体目标来解决这一关键障碍。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FIRE-SEQ)映射到基因组上多个位置的读取(多次读取)。这将能够对重复地区的监管要素进行编目。在目标2中，我们将通过同时利用多个相关的*-SEQ数据集来提高从OU概率模型中发现的重复区域的特异性。具体来说，我们 将制定方法，以监督外部芯片序列数据集对芯片和RNA序列数据集的分析。这将有助于对序列接近相同的重复元件进行准确的推断，例如片段重复、长时间散布的核元件，并提高RNA-SEQ对基因和异构体定量的准确性。在目标3中，我们将重点识别共占区/富集区，以推断区域/基因及其调控谱的细胞特异性模块。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。这将阐明调控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AIMS 1-3的方法应用于相关的ENCODE数据，以了解GATA因子在造血和血管生物学中的功能。人和小鼠的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强大的软件传播，将有助于加强和最大限度地发挥ENCODE得出的数据对生物医学研究界的影响。

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.