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: 8605900
• 负责人: Sunduz Keles
• 金额: $ 29.95万
• 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 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8605900
• 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; 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; Simulate; 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 association study; genome-wide; human disease; next generation sequencing; novel; programs; public health relevance; 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）：高吞吐量下一代测序（NGS）技术的出现通过允许快速且廉价的数十亿个碱基的测序来彻底改变了遗传学和基因组学领域。在NGS应用程序中，ChIP-Seq（染色质免疫沉淀和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映射。这两种都以细胞特异性方式在基因表达的编程中起着至关重要的作用。因此，它们的全基因组映射可以显着提高我们理解和诊断人类疾病的能力。尽管用于CHIP-SEQ数据的基本分析工具正在迅速增加，但所有可用方法都共享以下一个或多个缺点。首先，他们专注于一次分析一个芯片样本。随着chip-seq通常在表观基因组映射中使用以了解表型变化，因此对可以有效处理多个样品的方法的需求迅速上升。其次，他们仅利用序列读取的序列与参考基因组上的独特位置保持一致。这阻碍了Chip-Seq对基因组高度重复区域的研究。第三，用于CHIP-SEQ实验的常用设计使用每个芯片隔离样品采用一个匹配的控制样品。这限制了控制实验的基因组覆盖范围，并影响了芯片样品中富集的检测。它还显着有助于大规模芯片研究的测序成本增加。该项目的目的是在三个具体目的中应对CHIP-SEQ分析的这些挑战：（1）从多个样本进行推断的统计方法； （2）用于利用读取的概率模型，以映射到基因组中的多个位置（多阅读）； （3）用于控制实验的计算机合并设计中的开发和评估。这些项目将通过方法论发展，仿真，计算分析和实验验证的结合来完成。将使用来自编码，Modencode和路线图表观基因组学联盟的数据集以及合作者的新数据集开发和评估方法。该项目产生的统计资源将在公开可用的软件中传播，将为芯片seq实验的有效设计和分析提供必不可少的工具。