Advanced search
Publication Cover
Epigenetics Volume 15, 2020 - Issue 1-2
Submit an article Journal homepage
4,473
Views
21
CrossRef citations to date
0
Altmetric
Research Paper

Battle of epigenetic proportions: comparing Illumina’s EPIC methylation microarrays and TruSeq targeted bisulfite sequencing

Jonathan A. HeissDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-1448-2509View further author information
ORCID Icon,
Kasey J. BrennanDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY, USAView further author information
,
Andrea A. BaccarelliDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY, USAView further author information
,
Martha M. Téllez-RojoCenter for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, MexicoView further author information
,
Guadalupe Estrada-GutiérrezNational Institute of Perinatology, Mexico City, MexicoORCID Iconhttps://orcid.org/0000-0001-9551-9021View further author information
ORCID Icon,
Robert O. WrightDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USAView further author information
&
Allan C. JustDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USAORCID Iconhttps://orcid.org/0000-0003-4312-5957View further author information
ORCID Icon show all
Pages 174-182 | Received 17 Apr 2019, Accepted 08 Aug 2019, Published online: 05 Sep 2019

References

  • Simpkin AJ, Suderman M, Gaunt TR, et al. Longitudinal analysis of DNA methylation associated with birth weight and gestational age. Hum Mol Genet. 2015;24:3752–3763.
  • Xu C-J, Söderhäll C, Bustamante M, et al. DNA methylation in childhood asthma: an epigenome-wide meta-analysis. Lancet Respir Med. 2018;6:379–388.
  • Sun Z, Cunningham J, Slager S, et al. Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis. Epigenomics. 2015;7:813–828.
  • Teh AL, Pan H, Lin X, et al. Comparison of methyl-capture sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples. Epigenetics. 2016;11:36–48.
  • Heiss JA, Just AC. Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from GEO. Clin Epigenetics. 2018;10:73.
  • Heiss JA, Just AC. Improved filtering of DNA methylation microarray data by detection p values and its impact on downstream analyses. Clin Epigenetics. 2019;11:15.
  • Xu Z, Langie SAS, De Boever P, et al. RELIC: a novel dye-bias correction method for Illumina Methylation BeadChip. BMC Genomics. 2017;18:4.
  • Bibikova M, Barnes B, Tsan C, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98:288–295.
  • Kacmarczyk TJ, Fall MP, Zhang X, et al. “Same difference”: comprehensive evaluation of four DNA methylation measurement platforms. Epigenetics Chromatin. 2018;11:21.
  • Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 2012;13:R83.
  • Xu Z, Niu L, Li L, et al. ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res. 2016;44:e20.
  • McCartney DL, Walker RM, Morris SW, et al. Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genom Data. 2016;9:22–24.
  • Houseman EA, Accomando WP, Koestler DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
  • Kelly MB, Jason IF, Shan VA, et al. DNA methylation of cord blood cell types: Applications for mixed cell birth studies. Epigenetics. 2016;11(5).
  • Olivia MG, Hamid RR, Magda EP, et al. Nucleated red blood cells impact DNA methylation and expression analyses of cord blood hematopoietic cells. Clinical Epigenetics. 2015;7.
  • Chen D-P, Lin Y-C, Fann CSJ. Methods for identifying differentially methylated regions for sequence- and array-based data. Brief Funct Genomics. 2016;15:485–490.
  • Sofer T, Schifano ED, Hoppin JA, et al. A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure. Bioinformatics. 2013;29:2884–2891.
  • Jaffe AE, Murakami P, Lee H, et al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol. 2012;41:200–209.
  • Peters TJ, Buckley MJ, Statham AL, et al. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin. 2015;8:6.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.