Statistical Power Calculations for ChIP-seq experiments

ChIP-seq 实验的统计功效计算

基本信息

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
项目类别：
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

项目摘要

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数据的基础分析工具正在迅速增加，但在ChIP-seq实验的设计问题上却没有太大进展。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 和对照样本应该测序多深？答案取决于多个因素，其中一些因素可以由实验者根据试点/初步数据进行设置。 ChIP-seq 实验的测序深度是决定是否可以通过靶向能力识别所有潜在靶标（例如结合位置或表观基因组图谱）的关键因素之一。当目标是分析个体与个体之间以及转录因子结合和表观基因组谱的等位基因特异性变异时，这一点尤其重要。测序深度不足可能会导致结合或表观基因组谱出现虚假差异。在本提案中，我们的目标是通过考虑 ChIP-seq 分析中常用的统计模型，开发 ChIP-seq 实验功效计算的通用框架，具有三个具体目标：（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的功率计算工具。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 实验的高效设计提供必要的工具。公共健康相关性：拟议的研究与公共健康相关，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、分化和疾病非常有价值。 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 实验时的功效计算，并向研究界传播结果和软件。