▸ About

eFORGE

eFORGE was developed by Charles Breeze while on secondment at the European Bioinformatics Institute as part of the EpiTrain Initial Training Network.
The system is inspired by the FORGE tool developed by Ian Dunham.
The web interface was developed by Javier Herrero from the Bill Lyons Informatics Centre team.
The example data set corresponds to a filtered set of monocyte tDMPs from Jaffe AE and Irizarry RA, Genome Biol 2014, 15:R31.

(last updated on Thu Jan 11 12:06:42 2024)

Contact

Email Charles Breeze: c.breeze(at)ucl.ac.uk

eFORGE publications

eFORGE application and methodology is described at length in the following articles:
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 JH, 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 17(8):2137-2150. doi: 10.1016/j.celrep.2016.10.059
Breeze CE, Reynolds AP, van Dongen J, Dunham I, Lazar J, Neph S, Vierstra J, Bourque G, Teschendorff AE, Stamatoyannopoulos JA, Beck S (2019). eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data. Bioinformatics doi: 10.1093/bioinformatics/btz456
Breeze, C.E. (2021). Cell type-specific signal analysis in EWAS. BioRxiv 2021.05.21.445209. doi: 10.1101/2021.05.21.445209

Some of the papers that cite eFORGE

As of 27th of February 2017, eFORGE has either been referenced or applied by the following publications (for an updated list of references, see Google Scholar):
Stunnenberg HG; International Human Epigenome Consortium., Hirst M. (2016). The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery. Cell. 17;167(5):1145-1149. doi: 10.1016/j.cell.2016.11.007
Spurrell CH, Dickel DE, Visel A. (2016). The Ties That Bind: Mapping the Dynamic Enhancer-Promoter Interactome. Cell 167(5):1163-1166. doi: 10.1016/j.cell.2016.10.054. Review
Mendelson MM, Marioni RE, Joehanes R, Liu C, Hedman ÅK, Aslibekyan S, Demerath EW, Guan W, Zhi D, Yao C, Huan T, Willinger C, Chen B, Courchesne P, Multhaup M, Irvin MR, Cohain A, Schadt EE, Grove ML, Bressler J, North K, Sundström J, Gustafsson S, Shah S, McRae AF, Harris SE, Gibson J, Redmond P, Corley J, Murphy L, Starr JM, Kleinbrink E, Lipovich L, Visscher PM, Wray NR, Krauss RM, Fallin D, Feinberg A, Absher DM, Fornage M, Pankow JS, Lind L, Fox C, Ingelsson E, Arnett DK, Boerwinkle E, Liang L, Levy D, Deary IJ. (2017). Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach. PLoS Med.. 14(1):e1002215. doi: 10.1371/journal.pmed.1002215
Bujold D, Morais DA, Gauthier C, Côté C, Caron M, Kwan T, Chen KC, Laperle J, Markovits AN, Pastinen T, Caron B, Veilleux A, Jacques PÉ, Bourque G. (2016). The International Human Epigenome Consortium Data Portal. Cell Syst. 3(5):496-499.e2. doi: 10.1016/j.cels.2016.10.019
Johnson KC, Houseman EA, King JE, Christensen BC. (2017). DNA methylation differences at regulatory elements are associated with the cancer risk factor age in normal breast tissue. bioRxiv doi: 10.1101/101287
Li Y, Xu Q, Lv N, Wang L, Zhao H, Wang X, Guo J, Chen C, Li Y, Yu L. (2017). Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia. J Hematol Oncol. 2017 Feb 2;10(1):41. doi: 10.1186/s13045-017-0409-z. Review
Ligthart S, Marzi C, Aslibekyan S, Mendelson MM, Conneely KN, Tanaka T, Colicino E, Waite LL, Joehanes R, Guan W, Brody JA, Elks C, Marioni R, Jhun MA, Agha G, Bressler J, Ward-Caviness CK, Chen BH, Huan T, Bakulski K, Salfati EL; WHI-EMPC Investigators., Fiorito G; CHARGE epigenetics of Coronary Heart Disease., Wahl S, Schramm K, Sha J, Hernandez DG, Just AC, Smith JA, Sotoodehnia N, Pilling LC, Pankow JS, Tsao PS, Liu C, Zhao W, Guarrera S, Michopoulos VJ, Smith AK, Peters MJ, Melzer D, Vokonas P, Fornage M, Prokisch H, Bis JC, Chu AY, Herder C, Grallert H, Yao C, Shah S, McRae AF, Lin H, Horvath S, Fallin D, Hofman A, Wareham NJ, Wiggins KL, Feinberg AP, Starr JM, Visscher PM, Murabito JM, Kardia SL, Absher DM, Binder EB, Singleton AB, Bandinelli S, Peters A, Waldenberger M, Matullo G, Schwartz JD, Demerath EW, Uitterlinden AG, van Meurs JB, Franco OH, Chen YI, Levy D, Turner ST, Deary IJ, Ressler KJ, Dupuis J, Ferrucci L, Ong KK, Assimes TL, Boerwinkle E, Koenig W, Arnett DK, Baccarelli AA, Benjamin EJ, Dehghan A. (2016). DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biol. 17(1):255. doi: 10.1186/s13059-016-1119-5
Rodger EJ, Chatterjee A. (2017). The epigenomic basis of common diseases. Clin Epigenetics. 9:5. doi: 10.1186/s13148-017-0313-y
Lewis J, Breeze CE, Charlesworth J, Maclaren OJ, Cooper J. (2016). Where next for the reproducibility agenda in computational biology? BMC Syst. Biol. 10(1):52 doi: 10.1186/s12918-016-0288-x
Bartlett TE, Chindera K, McDermott J, Breeze CE, Cooke WR, Jones A, Reisel D, Karegodar ST, Arora R, Beck S, Menon U, Dubeau L, Widschwendter M. (2016). Epigenetic reprogramming of fallopian tube fimbriae in BRCA mutation carriers defines early ovarian cancer evolution. Nat. Commun. 7:11620. doi: 10.1038/ncomms11620
van Dongen J, Nivard MG, Willemsen G, Hottenga JJ, Helmer Q, Dolan CV, Ehli EA, Davies GE, van Iterson M, Breeze CE, Beck S; BIOS Consortium., Suchiman HE, Jansen R, van Meurs JB, Heijmans BT, Slagboom PE, Boomsma DI. (2016). Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat. Commun. 7:11115. doi: 10.1038/ncomms11115

Credits

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 JH, 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 17(8):2137-2150. doi: 10.1016/j.celrep.2016.10.059
Breeze CE, Reynolds AP, van Dongen J, Dunham I, Lazar J, Neph S, Vierstra J, Bourque G, Teschendorff AE, Stamatoyannopoulos JA, Beck S (2019). eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data. Bioinformatics doi: 10.1093/bioinformatics/btz456
Breeze, CE (2022). Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies. in Epigenome-Wide Association Studies: Methods and Protocols (ed. Guan, W.) 57-71 (Springer US). doi:10.1007/978-1-0716-1994-0_5 (bioRxiv preprint available at doi:10.1101/2021.05.21.445209)