Statistical Power Calculations for 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。这两者在细胞特异性基因表达的编程中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。虽然ChIP-seq数据的基本分析工具正在迅速增加，但关于ChIP-seq实验的设计问题并没有太大进展。计划ChIP-seq实验的研究人员需要回答的一个具有挑战性的问题是：ChIP和对照样品的测序深度应该是多少？答案取决于多个因素，其中一些可以由实验者根据试验/初步数据设置。ChIP-seq实验的测序深度是决定是否所有潜在靶标（例如，结合位置或表观基因组谱）可以用靶向能力来鉴定。当目标是分析转录因子结合和表观基因组谱的个体到个体和等位基因特异性变异时，这是特别重要的。测序深度不足可能导致结合或表观基因组谱中的虚假差异。在本提案中，我们的目标是为ChIP-seq实验中的功效计算开发一个通用框架，具有三个特定目标，并考虑ChIP-seq分析中常用的统计模型：（1）基于条件二项式模型的功效计算;（2）基于泊松和负二项式回归模型的功效计算;（3）GALAXY和Bioconductor的功效计算工具。该项目将通过理论/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，将为高效设计ChIP-seq实验提供必要的工具。 公共卫生相关性：拟议的研究与公共卫生有关，因为通过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了解发育，分化和疾病非常宝贵。ChIP-seq实验的设计提出了前所未有的挑战。我们将开发一个统计框架，用于设计ChIP-seq实验的功效计算，并将结果和软件传播给研究界。

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.