Statistical Analysis Methods and Software for ChIP-seq Data

ChIP-seq 数据的统计分析方法和软件

• 批准号: 8785690
• 负责人: Sunduz Keles
• 金额: $ 29.8万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-26 至 2016-09-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8785690
• 关键词: Address; Binding; Bioconductor; Biological; Cells; ChIP-on-chip; ChIP-seq; Characteristics; Communities; Computer Analysis; Computer Simulation; Computer software; Data; Data Set; Databases; Detection; Development; Diagnosis; Disease; Epigenetic Process; Evaluation; Experimental Designs; Galaxy; Gene Expression; Generations; Genetic; Genome; Genomics; Health; Histones; Individual; Investigation; Lead; Letters; Location; Maps; Methodology; Methods; Pattern; Play; Population; Positioning Attribute; Public Health; Reading; Repetitive Sequence; Research; Research Personnel; Resources; Role; Sampling; Statistical Methods; Statistical Models; Technology; Time; Tissues; Training; United States National Institutes of Health; Validation; Variant; base; chromatin immunoprecipitation; comparative; cost; design; epigenome; epigenomics; genome-wide; genome-wide analysis; human disease; next generation sequencing; novel; programs; research study; simulation; tool; transcription factor; transcriptome sequencing

DESCRIPTION (provided by applicant): The advent of high throughput next generation sequencing (NGS) technologies have revolutionized the fields of genetics and genomics by allowing rapid and inexpensive sequencing of billions of bases. Among the NGS applications, ChIP-seq (chromatin immunoprecipitation followed by NGS) is perhaps the most successful to date. ChIP-seq technology enables investigators to study genome-wide binding of transcription factors and mapping of epigenomic marks. Both of these play crucial roles in programming of gene expression in a cell specific manner; therefore their genome-wide mapping can significantly advance our ability to understand and diagnose human diseases. Although basic analysis tools for ChIP-seq data are rapidly increasing, all of the available methods share one or more of the following shortcomings. First, they focus on analyzing one ChIP- seq sample at a time. As ChIP-seq is becoming commonly utilized in epigenome mapping to understand phenotypic variation, the demand for methods that can handle multiple samples efficiently is rapidly rising. Second, they only utilize sequence reads that align to unique locations on the reference genome. This hinders the study of highly repetitive regions of genomes by ChIP-seq. Third, commonly used designs for ChIP-seq experiments employ one matching control sample per each ChIP-seq sample. This limits the genome coverage of control experiments and impacts the detection of enrichment in ChIP samples. It also significantly contributes to increase in sequencing costs for large-scale ChIP-seq studies. The objective of this project is to address these challenges of ChIP-seq analysis in three specific aims: (1) Statistical methods for inference from multiple samples; (2) Probabilistic models for utilizing reads that map to multiple locations (multi-reads) in the genome; (3) Development and evaluation of in silico pooling designs for control experiments. The projects will be accomplished through a combination of methodological development, simulation, computational analysis, and experimental validation. Methods will be developed and evaluated using datasets from the ENCODE, modENCODE, and the RoadMap Epigenomics consortiums as well as novel datasets from collaborators. Statistical resources generated from the project, which will be disseminated in publicly available software, will provide essential tools for the efficient design and analysis of ChIP-seq experiments.

描述（由申请人提供）：高通量下一代测序（NGS）技术的出现通过允许对数十亿个碱基进行快速且廉价的测序而彻底改变了遗传学和基因组学领域。在NGS应用中，ChIP-seq（染色质免疫沉淀，然后是NGS）可能是迄今为止最成功的。ChIP-seq技术使研究人员能够研究转录因子的全基因组结合和表观基因组标记的定位。这两种基因在以细胞特异性方式编程基因表达中起着至关重要的作用;因此，它们的全基因组图谱可以显着提高我们理解和诊断人类疾病的能力。虽然ChIP-seq数据的基本分析工具正在迅速增加，但所有可用的方法都有以下一个或多个缺点。首先，他们专注于一次分析一个ChIP-seq样本.随着ChIP-seq在表观基因组作图中被广泛用于了解表型变异，对能够有效处理多个样本的方法的需求正在迅速上升。其次，它们仅利用与参考基因组上的独特位置比对的序列读段。这阻碍了通过ChIP-seq研究基因组的高度重复区域。第三，ChIP-seq实验的常用设计为每个ChIP-seq样品采用一个匹配的对照样品。这限制了对照实验的基因组覆盖率，并影响了ChIP样品中富集的检测。它还显著增加了大规模ChIP-seq研究的测序成本。该项目的目标是解决ChIP-seq分析在三个具体目标中的这些挑战：（1）从多个样本进行推断的统计方法;（2）利用映射到基因组中多个位置的读段（多读段）的概率模型;（3）开发和评估用于对照实验的计算机合并设计。这些项目将通过方法开发、模拟、计算分析和实验验证相结合来完成。方法将使用ENCODE，modENCODE和RoadMap表观基因组学联盟的数据集以及合作者的新数据集进行开发和评估。该项目产生的统计资源将通过公开软件传播，将为ChIP-seq实验的有效设计和分析提供必要的工具。