Integrated analysis of genetic variation and epigenomic data

项目总结： 绝大多数遗传变异(GV)发生在非编码区，特别是基因间隔区。 了解GV的功能并揭示它们对基因表达的调控影响仍然是一个 巨大的挑战，因为将GV与它们的目标基因联系起来并考虑 单独的GV。大量ENCODE和路线图表观基因组学项目数据的可获得性 为应对这些挑战提供了前所未有的机遇。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相结合 数据包括组蛋白修饰、DNA结合蛋白芯片序列、RNA序列和开放染色质数据 构建代表细胞类型特定调控相互作用的遗传网络。这些网络将成为 用于注释患者样本中识别的GV，以揭示与疾病相关的GV。一旦完成， 拟议的研究将提供一套新的计算方法，用于综合分析 对路线图数据进行编码并建立摘要编码/路线图资源 表观基因组学项目数据。所提出的计算框架是通用的，可以很容易地应用于 其他公开数据。

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.