eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
| العنوان: | eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data |
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| المؤلفون: | 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 |
| المساهمون: | 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) |
| المصدر: | 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.2016.10.059Test 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 |
| بيانات النشر: | Elsevier BV, 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Resource, 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 |
| الوصف: | 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.ukTest/), 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/IHECTest. |
| وصف الملف: | application/pdf; application/vnd.openxmlformats-officedocument.wordprocessingml.document |
| تدمد: | 2211-1247 |
| DOI: | 10.1016/j.celrep.2016.10.059 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1a1e52e2c2614a6946bb8c084d45ecdTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e1a1e52e2c2614a6946bb8c084d45ecd |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22111247 |
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| DOI: | 10.1016/j.celrep.2016.10.059 |