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快速且廉价地对数十亿个碱基进行测序，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，然后进行 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图谱。这两者在以细胞特异性方式进行基因表达编程中都发挥着至关重要的作用。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。尽管 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存在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增长。其次，他们仅利用与参考基因组上的独特位置对齐的序列读取。这阻碍了通过 ChIP-seq 对基因组高度重复区域的研究。第三，ChIP-seq 实验的常用设计为每个 ChIP-seq 样本采用一个匹配的对照样本。这限制了对照实验的基因组覆盖范围，并影响 ChIP 样品中富集的检测。它还显着增加了大规模 ChIP-seq 研究的测序成本。该项目的目标是通过三个具体目标解决 ChIP-seq 分析的这些挑战：（1）从多个样本推断的统计方法； (2) 利用映射到基因组中多个位置的读取（多重读取）的概率模型； (3) 用于控制实验的计算机模拟池设计的开发和评估。这些项目将通过方法开发、模拟、计算分析和实验验证的结合来完成。将使用来自 ENCODE、modENCODE 和 RoadMap Epigenomics 联盟的数据集以及来自合作者的新颖数据集来开发和评估方法。该项目产生的统计资源将在公开软件中传播，将为 ChIP-seq 实验的高效设计和分析提供必要的工具。