Novel statistical tools for cell line specific epigenetic analysis

用于细胞系特异性表观遗传分析的新型统计工具

• 批准号: 8825711
• 负责人: Wenxuan Zhong
• 金额: $ 34.85万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8825711
• 关键词: Aberrant DNA Methylation; Affect; Algorithms; Alzheimer' s Disease; Angelman Syndrome; Autoimmune Diseases; Cell Line; Chronic Lymphocytic Leukemia; Communities; Complex; Computer software; DNA Methylation; Data; Data Analyses; Development; Diabetes Mellitus; Dimensions; Disease; Epigenetic Process; Funding; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Genes; Genetic Transcription; Genomics; Goals; Grant; Heart Diseases; Human; Intervention; Lead; Malignant Neoplasms; Methylation; Modeling; Modification; Nucleotides; Pattern; Prader-Willi Syndrome; Procedures; Process; Property; Regulation; Research; Role; Sensitivity and Specificity; Solutions; Specificity; Statistical Methods; Statistical Models; Techniques; Technology; Testing; Theoretical Studies; Transcriptional Regulation; Weight; age related; analytical method; base; cell type; computerized tools; design; flexibility; genetic regulatory protein; genome wide association study; genome-wide; high throughput analysis; human disease; improved; next generation sequencing; novel; public health relevance; theories; tool; transcription factor; transcriptome sequencing; user-friendly

DESCRIPTION (provided by applicant): Gene transcription is a complex and tightly regulated process. Accumulating evidence has indicated that it was concertedly regulated by regulatory proteins, mainly transcription factors (TF), and epigenetic modifications. The role of TFs in the regulation of gene transcription has been extensively studied, but much less understood is the role of epigenetic modification. DNA methylation has been newly discovered as key controller in gene transcription too. Aberrant DNA methylation changes can cause a number of human diseases such as developmental diseases (ICF syndrome, Prader-Willi and Angelman syndromes etc), aging related diseases (i.e. Alzheimer's disease), heart disease, diabetes, and autoimmune diseases. Moreover, large amount of evidence implicated that DNA methylation is a key player in cancer development. The overarching goal of this project is to develop a set of novel statistical tools to identify the differentially expressed DNA methylation patterns and understand the roles of DNA methylation in gene transcriptional regulation. In particular, we intends to achieve three scientific goals: 1) improving the sensitivity and specificity in identifyng the single nucleotide based DNA methylation change; 2) bridging the research gap in DNA methylation analysis and gene expression analysis by using the sufficient dimension reduction model; 3) developing a new statistical framework to overcome the grand challenges in epigenetic analysis and build the mathematical underpinning. With the rapid development of next generation sequencing technique in the past decades, where large amount of epigenetic and genomic data are routinely collected, processed and stored, we believe our efforts will not only extend our understanding of the regulatory mechanism in gene transcription but also lead to (1) fundamental advances in DNA methylation analysis, (2) development and refinement of technology for the rapid and continuous identification of gene regulation related DNA methylation cites, (3) prototyping of the epigenetic chip for human intervention of certain disease.

描述（由申请人提供）：基因转录是一个复杂且严格调节的过程。积累的证据表明，它是由调节蛋白（主要是转录因子（TF）和表观遗传修饰）协调一致的。 TF在基因转录调节中的作用已经进行了广泛的研究，但了解的是表观遗传修饰的作用。 DNA甲基化也已被新发现为基因转录的关键控制器。异常的DNA甲基化变化会导致许多人类疾病，例如发育疾病（ICF综合征，prader-Willi和Angelman综合征），相关疾病（即阿尔茨海默氏病），心脏病，糖尿病和自身免疫性疾病。此外，大量证据表明DNA甲基化是癌症发展的关键人物。该项目的总体目标是开发一组新型的统计工具，以识别差异表达的DNA甲基化模式，并了解DNA甲基化在基因转录调控中的作用。特别是，我们打算实现三个科学目标：1）提高识别基于单核苷酸的DNA甲基化变化的灵敏度和特异性； 2）通过使用足够的降低模型来弥合DNA甲基化分析和基因表达分析中的研究差距； 3）开发一个新的统计框架，以克服表观遗传分析的巨大挑战并建立数学基础。随着过去几十年中下一代测序技术的快速发展，常规收集，处理和存储了大量的表观遗传和基因组数据，我们的努力不仅会扩展我们对基因转录中的调节机制的理解，而且还会导致（1）在DNA甲基化分析中的基本进步，（2）在DNA甲基化分析中的基本进步，（2）甲基甲基甲基化的发展，（2） （3）对某些疾病的人类干预的表观遗传芯片的原型制作。