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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

基本信息

批准号：
8666661
负责人：
Hongkai Ji
金额：
$ 31.75万
依托单位：
JOHNS HOPKINS UNIVERSITY
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：
2012-09-12 至 2016-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8666661
关键词：
Accounting Address Alleles Antibodies Benchmarking Binding Binding Sites Biological ChIP-seq 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 High-Throughput Nucleotide Sequencing 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

项目摘要

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.

描述（由申请人提供）：ChIP-seq是一种强大的技术，用于绘制全基因组蛋白质-DNA相互作用（PDI）。世界各地的科学家越来越多地使用它来研究基因活动在正常细胞中是如何控制的，以及为什么它们在疾病中被破坏。应用ChIP-seq研究基因调控面临三大挑战：（1）如何分析大的ChIP-seq数据集以发现跨不同生物背景的基因调控的动态变化，（2）如何在对所有转录因子（TF）进行ChIP-seq不可行的实际约束下推断全局调控程序，以及（3）在杂合子SNP处的少量数据导致低统计功效的情况下，如何分析等位基因特异性事件。本研究探讨了新的统计和计算解决方案应对上述挑战。首先，将开发一种新的方法来发现和表征不同生物背景下基因调控的动态变化。这种方法，广义差分主成分分析（dPCA/GDPCA），集成了无监督模式发现，降维和统计推断到一个单一的统计框架。它提供了一个系统的解决方案，用于分析涉及多种蛋白质的大型ChIP-seq数据集的定量和曲线形状变化。预计它将有广泛的应用。其次，将开发一个计算框架来预测全球基因调控动态，即，所有TF下游调控事件的动态变化，其中DNA结合基序信息可用。该分析整合了组蛋白修饰ChIP-seq，DNase-seq和FAIRE-seq数据的动态变化与DNA序列，公共ChIP-seq和公共基因表达数据。它将为利用ChIP-seq同时研究多个TF提供实用、经济且相当准确的解决方案。还将进行系统的基准研究，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导。第三，将开发用于检测等位基因特异性蛋白质-DNA结合（ASB）的方法。该方法能够整合来自多个ChIP-seq数据集和完全定相的基因组序列的信息，以显着提高ASB推断的统计能力。还将处理各种偏见来源。本研究产生的指南和新的分析工具将允许人们在未来设计信息丰富的ChIP-seq实验，以便通过收集一组ChIP-seq数据，不仅可以识别PDI的位置，而且可以推断不同生物背景下TF结合位点的全局动态变化，并且如果基因型数据可用，则可以稳健地分析等位基因特异性基因调控。这将使ChIP-seq成为一个低成本高回报的实验，可用于多种目的。通过显著扩展ChIP-seq的实用性和增强其功能，我们的计算基础设施有望对推进未来的基因调控研究和人类疾病背后的调控机制的剖析产生重大影响。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Compressive Network Analysis.

压缩网络分析

DOI：
10.1109/tac.2014.2351712
发表时间：
2014-11
期刊：
IEEE transactions on automatic control
影响因子：
6.8
作者：
Jiang X;Yao Y;Liu H;Guibas L
通讯作者：
Guibas L

Gata6 potently initiates reprograming of pluripotent and differentiated cells to extraembryonic endoderm stem cells.

DOI：
10.1101/gad.257071.114
发表时间：
2015-06-15
期刊：
Genes & development
影响因子：
10.5
作者：
Wamaitha SE;del Valle I;Cho LT;Wei Y;Fogarty NM;Blakeley P;Sherwood RI;Ji H;Niakan KK
通讯作者：
Niakan KK

Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming.