Statistical and Computational Tools for Next-generation ChIP-seq Applications

用于下一代 ChIP-seq 应用的统计和计算工具

• 批准号: 8342445
• 负责人: Hongkai Ji
• 金额: $ 32.4万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-12 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8342445
• 关键词: Accounting; Address; Alleles; Antibodies; Benchmarking; Binding; Binding Sites; Biological; Complex; Coupled; Couples; DNA Binding; DNA Sequence; DNA-Binding Proteins; DNA-Protein Interaction; Data; Data Analyses; Data Set; Deoxyribonucleases; Dimensions; Disease; Dissection; Event; Experimental Designs; Face; Future; Gene Expression; Gene Expression Regulation; Generations; Genes; Genome; Genomics; Genotype; Global Change; Grant; Guidelines; Heterozygote; Human; Location; Maps; Methods; Normal Cell; Pattern; Performance; Phase; Principal Component Analysis; Protein Dynamics; Proteins; Public Domains; Reading; Regulator Genes; Research Infrastructure; Research Personnel; Resources; Sampling; Scientist; Shapes; Site; Software Tools; Solutions; Source; Technology; Transcription Factor 3; Vision; analytical method; analytical tool; chromatin immunoprecipitation; chromatin modification; computer framework; computer infrastructure; computerized tools; cost; design; follow-up; functional genomics; genome sequencing; genome-wide; high reward; histone modification; human disease; improved; innovation; method development; new technology; next generation; novel; novel strategies; programs; research study; tool; transcription factor; user friendly software

DESCRIPTION (provided by applicant): ChIP-seq is a powerful technology to map genome-wide protein-DNA interactions (PDIs). It is increasingly used by scientists worldwide to study how gene activities are controlled in normal cells and why they are disrupted in diseases. Applying ChIP-seq to study gene regulation faces three major challenges: (1) how to analyze large ChIP-seq data sets to discover dynamic changes of gene regulation across different biological contexts, (2) how to infer global regulatory programs under the practical constraint that it is not feasible to conduct ChIP-seq for all transcription factors (TFs), and (3) how to analyze allele-specific events given the small amount of data at heterozygote SNPs which cause low statistical power. This study investigates novel statistical and computational solutions to address the challenges above. First, a new method will be developed to discover and characterize dynamic changes of gene regulation across different biological contexts. This method, Generalized Differential Principal Component Analysis (dPCA/GDPCA), integrates unsupervised pattern discovery, dimension reduction and statistical inference into a single statistical framework. It provides a systematic solution to analyze quantitative and curve shape changes in large ChIP-seq data sets involving multiple proteins. It is expected to have a wide range of applications. Second, a computational framework will be developed to predict global gene regulation dynamics, i.e., dynamic changes of downstream regulatory events of all TFs for which DNA binding motif information is available. The analysis integrates the dynamic changes of histone modification ChIP-seq, DNase-seq, and FAIRE-seq data with DNA sequences, public ChIP-seq, and public gene expression data. It will provide a practical, affordable, and reasonably accurate solution to utilizing ChIP-seq to study many TFs simultaneously. A systematic benchmark study will also be con- ducted to evaluate the impact of technologies, data types and analytical methods on prediction performance. This benchmark study will provide guidelines for designing informative future experiments. Third, a method for detecting allele-specific protein-DNA binding (ASB) will be developed. The method is able to integrate information from multiple ChIP-seq data sets and completely phased genome sequences to significantly improve the statistical power of ASB inference. Various sources of biases will also be handled. Guidelines and new analytical tools generated by this study will allow one to design informative ChIP-seq experiments in the future such that by collecting one set of ChIP-seq data, one can not only identify locations of PDIs, but also infer global dynamic changes of TF binding sites across different biological contexts, and, if genotype data are available, robustly analyze allele-specific gene regulation. This will make ChIP-seq a low-cost high-reward experiment that serves multiple purposes. By significantly expanding the utility and increasing the power of ChIP-seq, our computational infrastructure is expected to have a major impact on advancing future studies of gene regulation and dissections of regulatory mechanisms behind human diseases. PUBLIC HEALTH RELEVANCE: ChIP-seq is a powerful technology to analyze how genes' activities are controlled in normal cells and diseases. This proposal develops statistical and computational tools urgently needed by scientists to analyze large and complex ChIP-seq data sets. By allowing one to examine dynamic changes of global gene regulatory programs across different biological contexts, the new computational technologies developed in this proposal are expected to have a major impact on advancing future studies of regulatory mechanisms behind human diseases.

描述（由申请人提供）：CHIP-SEQ是绘制全基因组蛋白-DNA相互作用（PDIS）的强大技术。它越来越多地被全世界的科学家使用，以研究如何在正常细胞中控制基因活性，以及​​为什么它们在疾病中受到破坏。应用CHIP-SEQ研究基因调节面临三个主要挑战：（1）如何分析大型芯片seq数据集，以发现不同生物学环境中基因调节的动态变化，（2）如何在实际约束下推断全球监管程序，以至于对所有转录因素进行chip-serce shore（tfs andece shore）（tfs and）（tfs and tfs andece shore shore）（3）和（tfs a tfs sy）（tfs and）（tfs）（3）（3），以及（3）。杂合子SNP会导致低统计功率。 这项研究研究了新型的统计和计算解决方案 解决上述挑战。首先，将开发一种新方法，以发现和表征不同生物学环境中基因调节的动态变化。该方法是广义差异主成分分析（DPCA/GDPCA），将无监督的模式发现，降低维度和统计推断整合到一个单个统计框架中。它提供了一个系统的解决方案，以分析涉及多种蛋白质的大型芯片序列数据集中的定量和曲线形状变化。预计它将具有广泛的应用。其次，将开发一个计算框架来预测全局基因调控动力学，即所有TFS的下游调节事件的动态变化，其中DNA结合基序信息可获得。该分析将组蛋白修饰芯片seq，dnase-seq和faire-seq数据与DNA序列，公共芯片seq和公共基因表达数据的动态变化集成在一起。它将提供一种实用，负担得起且合理准确的解决方案，以同时利用CHIP-SEQ研究许多TF。还将进行一项系统的基准研究，以评估技术，数据类型和分析方法对预测性能的影响。这项基准研究将为设计信息丰富的未来实验提供指南。第三，将开发一种检测等位基因特异性蛋白-DNA结合（ASB）的方法。该方法能够整合来自多个CHIP-SEQ数据集的信息和完全分阶段的基因组序列，以显着提高ASB推断的统计能力。也将处理各种偏见来源。 这项研究生成的指南和新的分析工具将使人们可以在将来设计信息丰富的芯片seq实验，以便通过收集一组芯片seq数据，不仅可以识别PDI的位置，而且可以推断出在不同生物学上下文中的TF结合站点的全球动态变化，以及基因观念跨基因型数据的可用性，可以很好地分析基因型数据。这将使Chip-Seq成为具有多种用途的低成本高回报实验。通过显着扩大效用并提高芯片序列的力量，我们的计算基础设施有望对推进人类疾病背后的监管机制的未来研究和解剖学的研究产生重大影响。 公共卫生相关性：CHIP-SEQ是一项强大的技术，可以分析在正常细胞和疾病中如何控制基因活动。该建议开发了科学家迫切需要的统计和计算工具，以分析大型且复杂的芯片seq数据集。通过允许一个人在不同的生物环境中检查全球基因调节计划的动态变化，预计该提案中开发的新计算技术将对推进人类疾病背后的监管机制的未来研究产生重大影响。