eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
| Autor: | Jenny van Dongen, Joost H.A. Martens, James E. Barrett, Lee M. Butcher, Edo Vellenga, Ian Dunham, Mattia Frontini, Andrew E. Teschendorff, John Ambrose, Robert Lowe, Dirk S. Paul, Guillaume Bourque, Sadia Saeed, Charles E. Breeze, Jonathan Laperle, Vardhman K. Rakyan, Willem H. Ouwehand, Ewan Birney, Filomena Matarese, Anke K. Bergmann, Hendrik G. Stunnenberg, Pierre-Étienne Jacques, Reiner Siebert, Javier Herrero, Stephan Beck, Kate Downes, Valentina Iotchkova |
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| Přispěvatelé: | Paul, Dirk [0000-0002-8230-0116], Frontini, Mattia [0000-0001-8074-6299], Downes, Kate [0000-0003-0366-1579], Ouwehand, Willem [0000-0002-7744-1790], Apollo - University of Cambridge Repository, Biological Psychology, Guided Treatment in Optimal Selected Cancer Patients (GUTS), Stem Cell Aging Leukemia and Lymphoma (SALL) |
| Rok vydání: | 2016 |
| Předmět: |
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Epigenomics 0301 basic medicine False discovery rate Multiple Sclerosis BLOOD Statistics as Topic Cell type specific DNase I hypersensitive sites Genome-wide association study Computational biology Biology ENCODE General Biochemistry Genetics and Molecular Biology 03 medical and health sciences SDG 3 - Good Health and Well-being Humans REGULATORY DNA Epigenetics EPIGENETIC SIGNATURE Molecular Biology GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries) Genetics SJOGRENS-SYNDROME epigenetics histone marks Stem Cells ASSOCIATION bioinformatics DNA Methylation epigenome-wide association study CANCER Disease etiology FALSE DISCOVERY RATE RHEUMATOID-ARTHRITIS 030104 developmental biology Organ Specificity Karyotyping DNA methylation WIDE DNA METHYLATION NAIVE CD4+T CELLS Software Genome-Wide Association Study Signal Transduction |
| Zdroj: | 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 E, Bourque, G, Bergmann, A K, Siebert, R, Vellenga, E, Saeed, S, Matarese, F, Martens, J H, Stunnenberg, H G, Teschendorff, A E, Herrero, J L, 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 Cell Reports, 17(8), 2137-2150. Cell Press Cell Reports Cell Reports, 17, 2137-2150 Cell Reports, 17, 8, pp. 2137-2150 Cell reports, 17(8), 2137-2150. CELL PRESS |
| ISSN: | 2211-1247 |
| DOI: | 10.1016/j.celrep.2016.10.059 |
| Popis: | Summary 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. Graphical Abstract Highlights • 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. |
| Databáze: | OpenAIRE |
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