MBRidge: an accurate and cost-effective method for profiling DNA methylome at single-base resolution

J Mol Cell Biol. 2015 Aug;7(4):299-313. doi: 10.1093/jmcb/mjv037. Epub 2015 Jun 15.

Abstract

Organisms and cells, in response to environmental influences or during development, undergo considerable changes in DNA methylation on a genome-wide scale, which are linked to a variety of biological processes. Using MethylC-seq to decipher DNA methylome at single-base resolution is prohibitively costly. In this study, we develop a novel approach, named MBRidge, to detect the methylation levels of repertoire CpGs, by innovatively introducing C-hydroxylmethylated adapters and bisulfate treatment into the MeDIP-seq protocol and employing ridge regression in data analysis. A systematic evaluation of DNA methylome in a human ovarian cell line T29 showed that MBRidge achieved high correlation (R > 0.90) with much less cost (∼10%) in comparison with MethylC-seq. We further applied MBRidge to profiling DNA methylome in T29H, an oncogenic counterpart of T29's. By comparing methylomes of T29H and T29, we identified 131790 differential methylation regions (DMRs), which are mainly enriched in carcinogenesis-related pathways. These are substantially different from 7567 DMRs that were obtained by RRBS and related with cell development or differentiation. The integrated analysis of DMRs in the promoter and expression of DMR-corresponding genes revealed that DNA methylation enforced reverse regulation of gene expression, depending on the distance from the proximal DMR to transcription starting sites in both mRNA and lncRNA. Taken together, our results demonstrate that MBRidge is an efficient and cost-effective method that can be widely applied to profiling DNA methylomes.

Keywords: DNA methylome; MB-seq; ridge regression; single-base resolution.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Base Sequence
  • Calibration
  • Cell Line, Tumor
  • Cost-Benefit Analysis
  • DNA Methylation / genetics*
  • Female
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Models, Biological
  • Ovarian Neoplasms / genetics
  • Regression Analysis
  • Reproducibility of Results
  • Sequence Analysis, DNA / economics*
  • Sequence Analysis, DNA / methods*
  • Software*