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

Charles E. Breeze; Dirk S. Paul; Jenny van Dongen; Lee M. Butcher; John C. Ambrose; James E. Barrett; Robert Lowe; Vardhman K. Rakyan; Valentina Iotchkova; Mattia Frontini; Kate Downes; Willem H. Ouwehand; Jonathan Laperle; Pierre Étienne Jacques; Guillaume Bourque; Anke K. Bergmann; Reiner Siebert; Edo Vellenga; Sadia Saeed; Filomena Matarese; Joost H.A. Martens; Hendrik G. Stunnenberg; Andrew E. Teschendorff; Javier Herrero; Ewan Birney; Ian Dunham; Stephan Beck

doi:10.1016/j.celrep.2016.10.059

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

Charles E. Breeze, Dirk S. Paul, Jenny van Dongen, Lee M. Butcher, John C. Ambrose, James E. Barrett, Robert Lowe, Vardhman K. Rakyan, Valentina Iotchkova, Mattia Frontini, Kate Downes, Willem H. Ouwehand, Jonathan Laperle, Pierre Étienne Jacques, Guillaume Bourque, Anke K. Bergmann, Reiner Siebert, Edo Vellenga, Sadia Saeed, Filomena MatareseJoost H.A. Martens, Hendrik G. Stunnenberg, Andrew E. Teschendorff, Javier Herrero, Ewan Birney, Ian Dunham, Stephan Beck

Research output: Contribution to journal › Article › peer-review

87 Citations (Scopus)

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.

Original language	English
Pages (from-to)	2137-2150
Number of pages	14
Journal	Cell Reports
Volume	17
Issue number	8
DOIs	https://doi.org/10.1016/j.celrep.2016.10.059
Publication status	Published - 15 Nov 2016
Externally published	Yes

Keywords

bioinformatics
DNase I hypersensitive sites
epigenetics
epigenome-wide association study
histone marks

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10.1016/j.celrep.2016.10.059

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Breeze, C. E., Paul, D. S., van Dongen, J., Butcher, L. M., Ambrose, J. C., Barrett, J. E., Lowe, R., Rakyan, V. K., Iotchkova, V., Frontini, M., Downes, K., Ouwehand, W. H., Laperle, J., Jacques, P. É., Bourque, G., Bergmann, A. K., Siebert, R., Vellenga, E., Saeed, S., ... Beck, S. (2016). eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data. Cell Reports, 17(8), 2137-2150. https://doi.org/10.1016/j.celrep.2016.10.059

@article{60ad2ab9cf064c1a888fdea7193ddf44,

title = "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.",

keywords = "bioinformatics, DNase I hypersensitive sites, epigenetics, epigenome-wide association study, histone marks",

author = "Breeze, {Charles E.} and Paul, {Dirk S.} and {van Dongen}, Jenny and Butcher, {Lee M.} and Ambrose, {John C.} and Barrett, {James E.} and Robert Lowe and Rakyan, {Vardhman K.} and Valentina Iotchkova and Mattia Frontini and Kate Downes and Ouwehand, {Willem H.} and Jonathan Laperle and Jacques, {Pierre {\'E}tienne} and Guillaume Bourque and Bergmann, {Anke K.} and Reiner Siebert and Edo Vellenga and Sadia Saeed and Filomena Matarese and Martens, {Joost H.A.} and Stunnenberg, {Hendrik G.} and Teschendorff, {Andrew E.} and Javier Herrero and Ewan Birney and Ian Dunham and Stephan Beck",

note = "Publisher Copyright: {\textcopyright} 2016 The Author(s)",

year = "2016",

month = nov,

day = "15",

doi = "10.1016/j.celrep.2016.10.059",

language = "English",

volume = "17",

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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 2016, 'eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data', Cell Reports, vol. 17, no. 8, pp. 2137-2150. https://doi.org/10.1016/j.celrep.2016.10.059

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T2 - A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data

AU - Breeze, Charles E.

AU - Paul, Dirk S.

AU - van Dongen, Jenny

AU - Butcher, Lee M.

AU - Ambrose, John C.

AU - Barrett, James E.

AU - Lowe, Robert

AU - Rakyan, Vardhman K.

AU - Iotchkova, Valentina

AU - Frontini, Mattia

AU - Downes, Kate

AU - Ouwehand, Willem H.

AU - Laperle, Jonathan

AU - Jacques, Pierre Étienne

AU - Bourque, Guillaume

AU - Bergmann, Anke K.

AU - Siebert, Reiner

AU - Vellenga, Edo

AU - Saeed, Sadia

AU - Matarese, Filomena

AU - Martens, Joost H.A.

AU - Stunnenberg, Hendrik G.

AU - Teschendorff, Andrew E.

AU - Herrero, Javier

AU - Birney, Ewan

AU - Dunham, Ian

AU - Beck, Stephan

PY - 2016/11/15

Y1 - 2016/11/15

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AB - 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.

KW - bioinformatics

KW - DNase I hypersensitive sites

KW - epigenetics

KW - epigenome-wide association study

KW - histone marks

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JO - Cell Reports

JF - Cell Reports

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ER -

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

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Keywords

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