A method of sample-wise region-set enrichment analysis for DNA methylomics

References

• Bock C . Analysing and interpreting DNA methylation data. Nat. Rev. Genet.13(10), 705–719 (2012).
   PubMed Web of Science ®Google Scholar
• Suelves M , CarrioE , Nunez-AlvarezY , PeinadoMA. DNA methylation dynamics in cellular commitment and differentiation. Brief. Funct. Genomics15(6), 443–453 (2016).
   PubMed Web of Science ®Google Scholar
• Varley KE , GertzJ , BowlingKMet al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res.23(3), 555–567 (2013).
   PubMed Web of Science ®Google Scholar
• Morris TJ , BeckS. Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data. Methods72, 3–8 (2015).
   PubMed Web of Science ®Google Scholar
• Yu X , SunS. Comparing five statistical methods of differential methylation identification using bisulfite sequencing data. Stat. Appl. Genet. Mol. Biol.15(2), 173–191 (2016).
   PubMed Web of Science ®Google Scholar
• Muller F , SchererM , AssenovYet al. RnBeads 2.0: comprehensive analysis of DNA methylation data. Genome Biol.20(1), 55 (2019).
   PubMedGoogle Scholar
• Geeleher P , HartnettL , EganLJ , GoldenA , RajaAli RA , SeoigheC. Gene-set analysis is severely biased when applied to genome-wide methylation data. Bioinformatics29(15), 1851–1857 (2013).
   PubMed Web of Science ®Google Scholar
• Phipson B , MaksimovicJ , OshlackA. missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics32(2), 286–288 (2016).
   PubMed Web of Science ®Google Scholar
• Li S , HeT , PawlikowskaI , LinT. Correcting length-bias in gene set analysis for DNA methylation data. Statistics Interface10(2), 279–289 (2017).
   Web of Science ®Google Scholar
• Ren X , KuanPF. methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing. Bioinformatics35(11), 1958–1959 (2019).
   PubMed Web of Science ®Google Scholar
• Wang Y , FranksJM , WhitfieldML , ChengC. BioMethyl: an R package for biological interpretation of DNA methylation data. Bioinformatics35(19), 3635–3641 (2019).
   PubMed Web of Science ®Google Scholar
• Dong D , TianY , ZhengSC , TeschendorffAE. ebGSEA: an improved Gene Set Enrichment Analysis method for epigenome-wide-association studies. Bioinformatics35(18), 3514–3516 (2019).
   PubMed Web of Science ®Google Scholar
• Subramanian A , TamayoP , MoothaVKet al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA102(43), 15545–15550 (2005).
   PubMed Web of Science ®Google Scholar
• Barbie DA , TamayoP , BoehmJSet al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature462(7269), 108–112 (2009).
   PubMed Web of Science ®Google Scholar
• Wang Z , WuX , WangY. A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip. BMC Bioinformatics19(Suppl. 5), 115 (2018).
   PubMedGoogle Scholar
• Lawson JT , TomazouEM , BockC , SheffieldNC. MIRA: an R package for DNA methylation-based inference of regulatory activity. Bioinformatics34(15), 2649–2650 (2018).
   PubMed Web of Science ®Google Scholar
• Sheffield NC , BockC. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics32(4), 587–589 (2016).
   PubMed Web of Science ®Google Scholar
• Lawson JT , SmithJP , BekiranovS , Garrett-BakelmanFE , SheffieldNC. COCOA: coordinate covariation analysis of epigenetic heterogeneity. Genome Biol.21(1), 240 (2020).
   PubMed Web of Science ®Google Scholar
• Sugiyama Y , GotohO , FukuiNet al. Two distinct tumorigenic processes in endometrial endometrioid adenocarcinoma. Am. J. Pathol.190(1), 234–251 (2020).
   PubMed Web of Science ®Google Scholar
• Kaneyasu T , MoriS , YamauchiHet al. Prevalence of disease-causing genes in Japanese patients with BRCA1/2-wildtype hereditary breast and ovarian cancer syndrome. NPJ Breast Cancer6(25), 1–13 (2020).
   PubMedGoogle Scholar
• Encode Project Consortium , MooreJE , PurcaroMJet al.Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature583(7818), 699–710 (2020).
   PubMed Web of Science ®Google Scholar
• Cheneby J , MenetrierZ , MestdaghMet al. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res.48(D1), D180–D188 (2020).
   PubMed Web of Science ®Google Scholar
• Sanchez-Castillo M , RuauD , WilkinsonACet al. CODEX: a next-generation sequencing experiment database for the haematopoietic and embryonic stem cell communities. Nucleic Acids Res.43(Database issue), D1117–1123 (2015).
   PubMedGoogle Scholar
• GenePattern webtool et al. (2020). www.genepattern.org/modules/docs/ssGSEAProjection/4
   Google Scholar
• Hannon E , MansellG , BurrageJet al. Assessing the co-variability of DNA methylation across peripheral cells and tissues: implications for the interpretation of findings in epigenetic epidemiology. bioRxiv doi:10.1101/2020.05.21.107730 (2020).
   PubMedGoogle Scholar
• Cancer Genome Atlas N . Comprehensive molecular portraits of human breast tumours. Nature490(7418), 61–70 (2012).
   PubMed Web of Science ®Google Scholar
• Fortin JP , TricheTJJr , HansenKDet al.. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics33(4), 558–560 (2017).
   PubMed Web of Science ®Google Scholar
• Fleischer T , TekpliX , MathelierAet al. DNA methylation at enhancers identifies distinct breast cancer lineages. Nat. Commun.8(1), 1379 (2017).
   PubMedGoogle Scholar
• Schwender H . Siggenes: Multiple Testing using SAM and Efron’s Empirical Bayes Approaches. R package version 1.60.0. doi:10.18129/B9.bioc.siggenes (2019).
   Google Scholar
• Zou LS , ErdosMR , TaylorDLet al. BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues. BMC Genomics19(1), 390 (2018).
   PubMedGoogle Scholar