Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm
- Bo Zhang1,
- Yan Zhou2,
- Nan Lin3,
- Rebecca F Lowdon1,
- Chibo Hong4,
- Raman P Nagarajan4,
- Jeffrey B Cheng4,
- Daofeng Li1,
- Michael Stevens1,
- Hyung Joo Lee1,
- Xiaoyun Xing1,
- Jia Zhou1,
- Vasavi Sundaram1,
- GiNell Elliott1,
- Junchen Gu1,
- Philippe Gascard4,
- Mahvash Sigaroudinia4,
- Thea D Tlsty4,
- Theresa Kadlecek4,
- Arthur Weiss4,
- Henriette O'Geen5,
- Peggy J Farnham6,
- Cecile L Maire7,
- Keith L Ligon7,
- Pamela AF Madden1,
- Angela Tam8,
- Richard Moore8,
- Martin Hirst8,
- Marco A Marra8,
- Baoxue Zhang2,
- Joseph F Costello4 and
- Ting Wang1,9
- 1 Washington University School of Medicine;
- 2 Northeast Normal University;
- 3 Washington University;
- 4 University of California San Francisco;
- 5 University of California Davis;
- 6 University of Southern California;
- 7 Dana-Farber Cancer Institute;
- 8 Canada's Michael Smith Genome Sciences Centre Vancouver
- ↵* Corresponding author; email: twang{at}genetics.wustl.edu
Abstract
DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression (Khulan et al. 2006; Suzuki and Bird 2008; Laird 2010; Day and Sweatt 2011; Jones 2012). Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. These include one of the most widely applied technologies for measuring DNA methylation, methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) (Weber et al. 2005; Maunakea et al. 2010), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq) (Maunakea et al. 2010). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been lacking. Here we present a novel integrative statistical framework M&M (for integration of MeDIP-seq and MRE-seq) that dynamically scales, normalizes and combines MeDIP-seq and MRE-seq data to detect differentially methylated regions. Using sample-matched whole-genome bisulfite sequencing (WGBS) as a gold standard, we demonstrate superior accuracy and reproducibility of M&M compared to existing analytical methods for MeDIP-seq data alone. M&M leverages the complementary nature of MeDIP-seq and MRE-seq data to allow rapid comparative analysis between whole methylomes at a fraction of the cost of WGBS. Comprehensive analysis of nineteen human DNA methylomes with M&M reveals distinct DNA methylation patterns among different tissue types, cell types, and individuals, potentially underscoring divergent epigenetic regulation at different scales of phenotypic diversity. We find that differential DNA methylation at enhancer elements, with concurrent changes in histone modifications and transcription factor binding, is common at the cell, tissue, and individual levels, whereas promoter methylation is more prominent in reinforcing fundamental tissue identities.
- Received February 18, 2013.
- Accepted June 13, 2013.
- © 2013, Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported), as described at http://creativecommons.org/licenses/by-nc/3.0/.











