eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
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 w...
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| Materiálatiipa: | Journal article |
| Giella: | English |
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Elsevier
2016
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| _version_ | 1826294920491565056 |
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| author | Breeze, C Paul, D van Dongen, J Butcher, L Ambrose, J Barrett, J Lowe, R Rakyan, V Iotchkova, V Frontini, M Downes, K Ouwehand, W Laperle, J Jacques, P Bourque, G Bergmann, A Siebert, R Vellenga, E Saeed, S Matarese, F Martens, J Stunnenberg, H Teschendorff, A Herrero, J Birney, E Dunham, I Beck, S |
| author_facet | Breeze, C Paul, D van Dongen, J Butcher, L Ambrose, J Barrett, J Lowe, R Rakyan, V Iotchkova, V Frontini, M Downes, K Ouwehand, W Laperle, J Jacques, P Bourque, G Bergmann, A Siebert, R Vellenga, E Saeed, S Matarese, F Martens, J Stunnenberg, H Teschendorff, A Herrero, J Birney, E Dunham, I Beck, S |
| author_sort | Breeze, C |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T03:53:08Z |
| format | Journal article |
| id | oxford-uuid:c1f6db82-2c18-40f5-a3b3-9d24e23233c2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:53:08Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:c1f6db82-2c18-40f5-a3b3-9d24e23233c22022-03-27T06:05:34ZeFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic DataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c1f6db82-2c18-40f5-a3b3-9d24e23233c2EnglishSymplectic Elements at OxfordElsevier2016Breeze, CPaul, Dvan Dongen, JButcher, LAmbrose, JBarrett, JLowe, RRakyan, VIotchkova, VFrontini, MDownes, KOuwehand, WLaperle, JJacques, PBourque, GBergmann, ASiebert, RVellenga, ESaeed, SMatarese, FMartens, JStunnenberg, HTeschendorff, AHerrero, JBirney, EDunham, IBeck, SEpigenome-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. |
| spellingShingle | Breeze, C Paul, D van Dongen, J Butcher, L Ambrose, J Barrett, J Lowe, R Rakyan, V Iotchkova, V Frontini, M Downes, K Ouwehand, W Laperle, J Jacques, P Bourque, G Bergmann, A Siebert, R Vellenga, E Saeed, S Matarese, F Martens, J Stunnenberg, H Teschendorff, A Herrero, J Birney, E Dunham, I Beck, S eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data |
| title | eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data |
| title_full | eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data |
| title_fullStr | eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data |
| title_full_unstemmed | eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data |
| title_short | eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data |
| title_sort | eforge a tool for identifying cell type specific signal in epigenomic data |
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