Statistical Methods for the Analysis of ChlP-chip Data

ChlP 芯片数据分析的统计方法

• 批准号: 7253510
• 负责人: Sunduz Keles
• 金额: $ 28.24万
• 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 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7253510
• 关键词: Statistical; Methods; Analysis; ChlP; chip

DESCRIPTION (provided by applicant): With many genome-sequencing projects coming to an end, the biggest remaining challenge is to comprehend the information encoded in these sequences. Identifying interactions between transcription factors (TFs) and their DMA binding sites is an integral part of this challenge. These interactions control critical steps in cell functions, and their dysfunction can significantly contribute to the progression of various diseases. ChlP-chip experiments that couple chromatin immunoprecipitation with DMA microarray analysis have become powerful tools for the genome-wide identification and characterization of transcription factor binding sites. These experiments produce massive amounts of noisy data with small number of replicates and therefore require innovative robust statistical analysis methods. The objectives of this proposal are to develop, evaluate and disseminate statistical methods for analyzing data from ChlP-chip experiments. These objectives will be accomplished through four specific aims: (1) Development of robust probabilistic methods for detecting TF bound regions. These methods will utilize the information common across probes on tiling arrays to increase power in small sample sizes. (2) Extension of the methods in Aim-1 to deal with array designs where probe sequences overlap and observations from nearby probes exhibit long-range spatial dependencies. As a result, we will develop rigorous statistical inference procedures for general tiling array designs. (3) Development of an adaptive framework for incorporating quantitative information from ChlP-chip experiments into motif finding. This will connect the first stage of the ChlP-chip data analysis, namely identification of the bound regions, with the downstream sequence analysis thereby boosting the sensitivity and specificity of the motif finding task. (4) Implementation of the statistical methods developed as part of this research in statistical packages. The resulting packages will be available to the scientific community both in stand-alone versions and as part of the Bioconductor Project which is an open source and development software project for the analysis of the genomic data. Successful completion of the proposed research will result in substantially improved statistical methods for the analysis of ChlP-chip experiments.

描述（由申请人提供）：随着许多基因组测序项目即将结束，剩下的最大挑战是理解这些序列中编码的信息。识别转录因子 (TF) 与其 DMA 结合位点之间的相互作用是这一挑战的一个组成部分。这些相互作用控制着细胞功能的关键步骤，它们的功能障碍可以显着促进各种疾病的进展。将染色质免疫沉淀与 DMA 微阵列分析相结合的 ChlP 芯片实验已成为转录因子结合位点全基因组识别和表征的强大工具。这些实验会产生大量带有少量重复的噪声数据，因此需要创新的稳健统计分析方法。该提案的目标是开发、评估和传播用于分析 ChlP 芯片实验数据的统计方法。这些目标将通过四个具体目标来实现：（1）开发用于检测 TF 绑定区域的稳健概率方法。这些方法将利用平铺阵列上的探针通用的信息来提高小样本量的功效。 (2) 扩展 Aim-1 中的方法来处理探针序列重叠和附近探针的观察结果表现出远程空间依赖性的阵列设计。因此，我们将为一般平铺阵列设计开发严格的统计推断程序。 (3) 开发一个自适应框架，将 ChlP 芯片实验的定量信息纳入基序发现中。这将连接 ChlP 芯片数据分析的第一阶段，即结合区域的识别，与下游序列分析，从而提高基序寻找任务的敏感性和特异性。 (4) 在统计包中实施作为本研究的一部分开发的统计方法。由此产生的软件包将以独立版本和作为 Bioconductor 项目的一部分提供给科学界，Bioconductor 项目是一个用于分析基因组数据的开源和开发软件项目。成功完成拟议研究将大大改进 ChlP 芯片实验分析的统计方法。