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.

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