Statistical Methods for the Analysis of ChlP-chip Data

ChlP 芯片数据分析的统计方法

• 批准号: 7616521
• 负责人: Sunduz Keles
• 金额: $ 28.47万
• 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/7616521
• 关键词: Affinity; Algorithms; Base Pairing; Binding; Binding Sites; Bioconductor; Biologic Characteristic; Biological; Cell physiology; Characteristics; Collection; Communities; Comprehension; DNA; DNA Binding; DNA Microarray Chip; Data; Data Analyses; Data Set; Dependency; Development; Disease; Drosophila genus; Exhibits; Functional RNA; Functional disorder; General Transcription Factors; Genome; Genomics; Human; Human Genome; Indium; Internet; JUN gene; Length; Link; Location; Methodology; Methods; Microarray Analysis; Modeling; Mus; Nature; Oligonucleotide Microarrays; Play; Procedures; Rattus; Research; Research Personnel; Resolution; Reverse Transcriptase Polymerase Chain Reaction; Role; Running; Sample Size; Sensitivity and Specificity; Sequence Analysis; Simulate; Staging; Statistical Methods; Structure; Technology; Time; Training; Transcription Factor TFIID; Validation; c-Myc Staining Method; chromatin immunoprecipitation; cost; density; design; genome sequencing; genome wide association study; genome-wide; human GATA1 protein; human embryonic stem cell line; improved; innovation; novel; open source; programs; research study; response; serial analysis of gene expression; software development; tool; transcription factor; web site

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）：随着许多基因组序列项目即将结束，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组成部分。这些相互作用控制细胞功能中的关键步骤，它们的功能障碍可以显着有助于各种疾病的发展。将染色质免疫沉淀与DMA微阵列分析的CHLP-CHIP实验已成为转录因子结合位点的全基因组鉴定和表征的强大工具。这些实验可产生大量的嘈杂数据，并具有少量的重复数据，因此需要创新的鲁棒统计分析方法。该提案的目标是开发，评估和传播统计方法，以分析CHLP-CHIP实验的数据。这些目标将通过四个特定目的来实现：（1）开发用于检测TF边界区域的强大概率方法。这些方法将利用整个探针上常见的信息，以增加小样本量的功率。 （2）AIM-1中的方法扩展以处理阵列设计，其中探针序列重叠和附近探针的观察结果表现出长距离空间依赖性。结果，我们将为一般平铺阵列设计开发严格的统计推理程序。 （3）开发一个自适应框架，将CHLP-CHIP实验中的定量信息纳入基序发现。这将连接CHLP-CHIP数据分析的第一阶段，即对边界区域的识别，并将下游序列分析与下游序列分析联系起来，从而提高了基序查找任务的灵敏度和特异性。 （4）在统计软件包中开发的统计方法的实施。所得的包裹将以独立版本和生物导体项目的一部分提供给科学界，这是一个开源和开发软件项目，用于分析基因组数据。成功完成拟议的研究将导致大大改善CHLP-CHIP实验分析的统计方法。