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Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications.

  • Author(s): Harris, R Alan
  • Wang, Ting
  • Coarfa, Cristian
  • Nagarajan, Raman P
  • Hong, Chibo
  • Downey, Sara L
  • Johnson, Brett E
  • Fouse, Shaun D
  • Delaney, Allen
  • Zhao, Yongjun
  • Olshen, Adam
  • Ballinger, Tracy
  • Zhou, Xin
  • Forsberg, Kevin J
  • Gu, Junchen
  • Echipare, Lorigail
  • O'Geen, Henriette
  • Lister, Ryan
  • Pelizzola, Mattia
  • Xi, Yuanxin
  • Epstein, Charles B
  • Bernstein, Bradley E
  • Hawkins, R David
  • Ren, Bing
  • Chung, Wen-Yu
  • Gu, Hongcang
  • Bock, Christoph
  • Gnirke, Andreas
  • Zhang, Michael Q
  • Haussler, David
  • Ecker, Joseph R
  • Li, Wei
  • Farnham, Peggy J
  • Waterland, Robert A
  • Meissner, Alexander
  • Marra, Marco A
  • Hirst, Martin
  • Milosavljevic, Aleksandar
  • Costello, Joseph F
  • et al.

Published Web Location

https://doi.org/10.1038/nbt.1682
Abstract

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.

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