Dataset Information

EFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.

ABSTRACT: 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.

SUBMITTER: Breeze CE

PROVIDER: S-EPMC5120369 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Publications

eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.

Breeze Charles E CE Paul Dirk S DS van Dongen Jenny J Butcher Lee M LM Ambrose John C JC Barrett James E JE Lowe Robert R Rakyan Vardhman K VK Iotchkova Valentina V Frontini Mattia M Downes Kate K Ouwehand Willem H WH Laperle Jonathan J Jacques Pierre-Étienne PÉ Bourque Guillaume G Bergmann Anke K AK Siebert Reiner R Vellenga Edo E Saeed Sadia S Matarese Filomena F Martens Joost H A JHA Stunnenberg Hendrik G HG Teschendorff Andrew E AE Herrero Javier J Birney Ewan E Dunham Ian I Beck Stephan S

Cell reports 20161101 8

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 a ...[more]

PMID: 27851974

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Project description:MotivationThe Human Reference Epigenome Map, generated by the Roadmap Epigenomics Consortium, contains thousands of genome-wide epigenomic datasets that describe epigenomes of a variety of different human tissue and cell types. This map has allowed investigators to obtain a much deeper and more comprehensive view of our regulatory genome, e.g. defining regulatory elements including all promoters and enhancers for a given tissue or cell type. An outstanding task is to combine and compare different epigenomes in order to identify regions with epigenomic features specific to certain types of tissues or cells, e.g. lineage-specific regulatory elements. Currently available tools do not directly address this question. This need motivated us to develop a tool that allows investigators to easily identify regions with epigenetic features unique to specific epigenomes that they choose, making detection of common regulatory elements and/or cell type-specific regulatory elements an interactive and dynamic experience.ResultsAn online tool EpiCompare was developed to assist investigators in exploring the specificity of epigenomic features across selected tissue and cell types. Investigators can design their test by choosing different combinations of epigenomes, and choosing different classification algorithms provided by our tool. EpiCompare will then identify regions with specified epigenomic features, and provide a quality assessment of the predictions. Investigators can interact with EpiCompare by investigating Roadmap Epigenomics data, or uploading their own data for comparison. We demonstrate that by using specific combinations of epigenomes we can detect developmental lineage-specific enhancers. Finally, prediction results can be readily visualized and further explored in the WashU Epigenome Browser.Availability and implementationEpiCompare is freely available on the web at http://epigenome.wustl.edu/EpiCompare/.Contacttwang@genetics.wustl.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

EFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.

Publications

eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.

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