eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.
eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.
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DOI：
10.1016/j.celrep.2016.10.059
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发表时间：
2016-11-15
期刊：
影响因子：
8.8
通讯作者：
Beck S
中科院分区：
文献类型：
--
作者：
Breeze CE;Paul DS;van Dongen J;Butcher LM;Ambrose JC;Barrett JE;Lowe R;Rakyan VK;Iotchkova V;Frontini M;Downes K;Ouwehand WH;Laperle J;Jacques PÉ;Bourque G;Bergmann AK;Siebert R;Vellenga E;Saeed S;Matarese F;Martens JHA;Stunnenberg HG;Teschendorff AE;Herrero J;Birney E;Dunham I;Beck S
表观全基因组关联研究（EWAS）通过考虑非遗传变异（如DNA甲基化改变）为研究人类疾病提供了另一种方法。为了推进EWAS的复杂解释，我们开发了eFORGE（http：//eforge.cs.ucl.ac.uk/），这是一个新的独立的基于Web的工具，用于分析和解释EWAS数据。eFORGE确定了一组EWAS鉴定的差异甲基化位置的细胞类型特异性调节组分。这是通过检测来自ENCODE、Roadmap表观基因组学和BLUEPRINT项目的454个样本（组织、原代细胞类型和细胞系）中与DNA酶I超敏位点重叠的富集来实现的。将eFORGE应用于20个公开可用的EWAS数据集，确定了几种常见疾病的疾病相关细胞类型，癌症中的干细胞样特征，并证明了检测EWAS对异质组织的细胞组成影响的能力。我们的方法弥合了大规模表观基因组学数据和EWAS衍生的靶点选择之间的差距，以深入了解疾病病因。EWAS数据分析和解释工具的开发异质EWAS数据中细胞类型特异性信号的识别EWAS中细胞组成效应的识别EWAS中20个已发表EWAS的eFORGE目录汇编Breeze et al.开发一个工具，用于分析和解释EWAS数据。eFORGE工具可以识别异质EWAS数据中的细胞类型特异性、疾病相关信号，还可以识别细胞组成效应。在Cell Press IHEC门户网站http://www.cell.com/consortium/IHEC上探索联盟数据。
Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology. Development of a tool for the analysis and interpretation of EWAS data Identification of cell type-specific signals in heterogeneous EWAS data Identification of cell-composition effects in EWAS Compilation of eFORGE catalog of 20 published EWAS Breeze et al. develop a tool for the analysis and interpretation of EWAS data. The eFORGE tool identifies cell type-specific, disease-relevant signals in heterogeneous EWAS data and can also identify cell-composition effects. Explore consortium data at the Cell Press IHEC webportal at http://www.cell.com/consortium/IHEC.
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