Literature DB >> 26078362

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

Wanshi Cai1, Fengbiao Mao1, Huajing Teng2, Tao Cai3, Fangqing Zhao4, Jinyu Wu5, Zhong Sheng Sun5.   

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.
© The Author (2015). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS. All rights reserved.

Entities:  

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

Mesh:

Year:  2015        PMID: 26078362      PMCID: PMC4542811          DOI: 10.1093/jmcb/mjv037

Source DB:  PubMed          Journal:  J Mol Cell Biol        ISSN: 1759-4685            Impact factor:   6.216


  61 in total

1.  CpG methylation modifies the genetic stability of cloned repeat sequences.

Authors:  Kerrie Nichol; Christopher E Pearson
Journal:  Genome Res       Date:  2002-08       Impact factor: 9.043

2.  Comparison of methyl-DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain (MBD) protein capture for genome-wide DNA methylation analysis reveal CpG sequence coverage bias.

Authors:  Shalima S Nair; Marcel W Coolen; Clare Stirzaker; Jenny Z Song; Aaron L Statham; Dario Strbenac; Mark D Robinson; Susan J Clark
Journal:  Epigenetics       Date:  2011-01-01       Impact factor: 4.528

Review 3.  DNA methylation and human disease.

Authors:  Keith D Robertson
Journal:  Nat Rev Genet       Date:  2005-08       Impact factor: 53.242

4.  MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment.

Authors:  Mattia Pelizzola; Yasuo Koga; Alexander Eckehart Urban; Michael Krauthammer; Sherman Weissman; Ruth Halaban; Annette M Molinaro
Journal:  Genome Res       Date:  2008-09-02       Impact factor: 9.043

5.  Systematic assessment of reduced representation bisulfite sequencing to human blood samples: A promising method for large-sample-scale epigenomic studies.

Authors:  Li Wang; Jihua Sun; Honglong Wu; Siyang Liu; Junwen Wang; Boxin Wu; Shujia Huang; Ning Li; Jun Wang; Xiuqing Zhang
Journal:  J Biotechnol       Date:  2011-07-06       Impact factor: 3.307

Review 6.  The host defence function of genomic methylation patterns.

Authors:  T H Bestor
Journal:  Novartis Found Symp       Date:  1998

7.  DNA methylation of the first exon is tightly linked to transcriptional silencing.

Authors:  Fabienne Brenet; Michelle Moh; Patricia Funk; Erika Feierstein; Agnes J Viale; Nicholas D Socci; Joseph M Scandura
Journal:  PLoS One       Date:  2011-01-18       Impact factor: 3.240

8.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Authors:  Mark D Robinson; Davis J McCarthy; Gordon K Smyth
Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

9.  Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells.

Authors:  Madeleine P Ball; Jin Billy Li; Yuan Gao; Je-Hyuk Lee; Emily M LeProust; In-Hyun Park; Bin Xie; George Q Daley; George M Church
Journal:  Nat Biotechnol       Date:  2009-03-29       Impact factor: 54.908

10.  BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach.

Authors:  Andrea Riebler; Mirco Menigatti; Jenny Z Song; Aaron L Statham; Clare Stirzaker; Nadiya Mahmud; Charles A Mein; Susan J Clark; Mark D Robinson
Journal:  Genome Biol       Date:  2014-02-11       Impact factor: 13.583

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  4 in total

Review 1.  DNA Methylation Analysis: Choosing the Right Method.

Authors:  Sergey Kurdyukov; Martyn Bullock
Journal:  Biology (Basel)       Date:  2016-01-06

2.  Genome-Wide Methylation Patterns in Androgen-Independent Prostate Cancer Cells: A Comprehensive Analysis Combining MeDIP-Bisulfite, RNA, and microRNA Sequencing Data.

Authors:  Yumin Wang; Tingting Qin; Wangqiang Hu; Binghua Chen; Meijie Dai; Gang Xu
Journal:  Genes (Basel)       Date:  2018-01-11       Impact factor: 4.096

3.  Somatic mutations in colorectal cancer are associated with the epigenetic modifications.

Authors:  Hongwei Lei; Kaixiong Tao
Journal:  J Cell Mol Med       Date:  2020-08-31       Impact factor: 5.310

4.  A new approach to decode DNA methylome and genomic variants simultaneously from double strand bisulfite sequencing.

Authors:  Jialong Liang; Kun Zhang; Jie Yang; Xianfeng Li; Qinglan Li; Yan Wang; Wanshi Cai; Huajing Teng; Zhongsheng Sun
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

  4 in total

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