Statistical Power Calculations for ChIP-seq experiments

ChIP-seq 实验的统计功效计算

• 批准号: 8284083
• 负责人: Sunduz Keles
• 金额: $ 18.41万
• 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: 2012-05-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8284083
• 关键词: Alleles; Base Sequence; Binding; Binomial Model; Bioconductor; Biologic Characteristic; Biological; Cells; ChIP-seq; Communities; Computer Analysis; Computer software; DNA; Data; Data Analyses; Data Set; Databases; Detection; Development; Diagnosis; Disease; Family; Gene Expression; Genetic; Genome; Genomics; Goals; Guanine + Cytosine Composition; Immune Sera; Immunoglobulin G; Individual; Lead; Letters; Location; Maps; Methods; Modeling; Play; Public Health; Reading; Research; Research Personnel; Resources; Role; Sampling; Simulate; Staging; Statistical Models; Technology; Tissues; Training; United States National Institutes of Health; Validation; Variant; base; chromatin immunoprecipitation; design; epigenomics; genome sequencing; genome wide association study; genome-wide; human disease; next generation; novel; programs; research study; simulation; software development; tool; transcription factor

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 cell specific gene expression; 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, there has not been much progress on the design problems regarding ChIP-seq experiments. A challenging question that the researchers planning a ChIP-seq experiment need to answer is: how deeply should the ChIP and the control samples be sequenced? The answer depends on multiple factors some of which can be set by the experimenter based on pilot/preliminary data. The sequencing depth of a ChIP-seq experiment is one of the key factors that determine whether or not all the underlying targets (e.g., binding locations or epigenomic profiles) can be identified with a targeted power. This is especially important when the goal is the analysis of individual-to-individual and allele specific variation o transcription factor binding and epigenomic profiles. Insufficient sequencing depths may lead to spurious differences in binding or epigenome profiles. In this proposal, we aim to develop a general framework for power calculations in ChIP-seq experiments with three specific aims and by considering statistical models commonly used in ChIP-seq analysis: (1) Power calculations based on the conditional Binomial model; (2) Power calculations based on the Poisson and Negative Binomial regression models; (3) A power calculation tool for GALAXY and Bioconductor. This project will be accomplished through a combination of theoretical/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 of ChIP-seq experiments. PUBLIC HEALTH RELEVANCE: The proposed research is relevant to public health because capturing genome-wide binding of transcription factors and epigenomic information by ChIP-seq technology is invaluable for comprehensively understanding development, differentiation, and disease. Design of ChIP-seq experiments present unprecedented challenges. We will develop a statistical framework for power calculations in designing ChIP-seq experiments and disseminate results and software to the research community.

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。这两个在细胞特异性基因表达的编程中都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，但是关于CHIP-SEQ实验的设计问题并没有太大进展。一个挑战性的问题是，计划芯片seq实验需要回答的研究人员是：对芯片和对照样本的测序应深入多？答案取决于多种因素，其中一些因素可以根据试验/初步数据来设置。 CHIP-SEQ实验的测序深度是确定是否可以用靶向功率来识别所有基础目标（例如结合位置或表观基因组概况）的关键因素之一。当目标是分析个体至个体和等位基因特异性变异O转录因子结合和表观基因组谱时，这一点尤其重要。测序深度不足可能导致结合或表观基因组谱的虚假差异。在此提案中，我们旨在在CHIP-SEQ实验中开发一个通用框架，以三个特定目的进行芯片seq实验，并考虑在芯片seq分析中常用的统计模型：（1）基于条件二项式模型的功率计算； （2）基于泊松和负二项式回归模型的功率计算； （3）银河系和生物导体的功率计算工具。该项目将通过理论/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实验的有效设计提供必不可少的工具。 公共卫生相关性：拟议的研究与公共卫生有关，因为Chip-Seq技术捕获转录因子和表观基因组信息的全基因组结合对于全面理解发展，分化和疾病是无价的。 Chip-Seq实验的设计提出了前所未有的挑战。我们将在设计CHIP-SEQ实验并将结果和软件传播给研究社区时为电源计算开发一个统计框架。