Integrated analysis of genetic variation and epigenomic data

遗传变异和表观基因组数据的综合分析

• 批准号: 9333639
• 负责人: Wei Wang
• 金额: $ 52.04万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-03 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9333639
• 关键词: Affect; Algorithms; Amino Acid Sequence; Binding; Binding Proteins; Binding Sites; Cancer Patient; Cell Line; Cells; ChIP-seq; Chromatin; Communities; Computing Methodologies; Critical Pathways; DNA Methylation; DNA-Binding Proteins; Data; Disease; Distal; Elements; Enhancers; Gene Expression; Gene Targeting; Genes; Genetic; Genetic Annotation; Genetic Variation; Genome; Human Genome; Individual; Intercistronic Region; Link; Malignant Neoplasms; Maps; Methods; Patients; Performance; Phenotype; Regulation; Research; Resolution; Resources; Role; Sampling; Sampling Studies; Scanning; Signal Pathway; Specificity; Suggestion; The Cancer Genome Atlas; Tissues; Untranslated RNA; cell type; chromatin protein; computer framework; differential expression; epigenome; epigenomics; experimental study; genetic analysis; genetic association; genome wide association study; histone modification; improved; innovation; new therapeutic target; novel; personalized medicine; promoter; tool; transcription factor; transcriptome sequencing

Project Summary: The large majority of genetic variations (GVs) occur in non-coding regions particularly in intergenic regions. Understanding the functions of the GVs and revealing their regulatory impact on gene expression remain a great challenge because it is not trivial to link GVs to their target genes and consider collaborative effect of individual GVs. The availability of large amount of the ENCODE and Roadmap Epigenomics Project data provides an unprecedented opportunity to tackle these challenges. We will develop new computational methods to predict long-range promoter-enhancer interactions from epigenomic data. These predicted promoter-enhancer interactions will be integrated with the other ENCODE and Roadmap Epigenomics Project data including histone modification, ChIP-seq of DNA binding proteins, RNA-seq and open chromatin data to construct genetic networks that represent cell-type specific regulatory interactions. These networks will be used to annotate the GVs identified in patient samples to reveal disease-related GVs. Once completed, the proposed study will provide a suite of new computational methods for integrative analysis of the ENCODE/Epigenome Roadmap data and establish a resource of the digested ENCODE/Roadmap Epigenomics Project data. The proposed computational framework is general and can be easily applied to other public data.

项目摘要： 绝大多数遗传变异（GV）发生在非编码区，特别是基因间区。 了解GVs的功能并揭示其对基因表达的调控作用仍然是一个挑战。 这是一个巨大的挑战，因为将GV与其靶基因联系起来并考虑 个人gv大量ENCODE和Roadmap表观基因组学项目数据的可用性 为应对这些挑战提供了前所未有的机会。我们将开发新的计算 从表观基因组数据预测远程启动子-增强子相互作用的方法。这些预测 启动子-增强子相互作用将与其他ENCODE和路线图表观基因组学项目整合 数据包括组蛋白修饰、DNA结合蛋白的ChIP-seq、RNA-seq和开放染色质数据， 构建代表细胞类型特异性调控相互作用的遗传网络。这些网络将 用于注释患者样本中识别的GV，以揭示疾病相关GV。一旦完成， 该研究将为综合分析这一问题提供一套新的计算方法。 ENCODE/表观基因组路线图数据并建立消化的ENCODE/路线图资源 表观基因组学项目数据。建议的计算框架是通用的，可以很容易地应用到 其他公共数据。