@article{Zhang26062013,
author = {Zhang, Bo and Zhou, Yan and Lin, Nan and Lowdon, Rebecca F and Hong, Chibo and Nagarajan, Raman P and Cheng, Jeffrey B and Li, Daofeng and Stevens, Michael and Lee, Hyung Joo and Xing, Xiaoyun and Zhou, Jia and Sundaram, Vasavi and Elliott, GiNell and Gu, Junchen and Gascard, Philippe and Sigaroudinia, Mahvash and Tlsty, Thea D and Kadlecek, Theresa and Weiss, Arthur and O'Geen, Henriette and Farnham, Peggy J and Maire, Cecile L and Ligon, Keith L and Madden, Pamela AF and Tam, Angela and Moore, Richard and Hirst, Martin and Marra, Marco A and Zhang, Baoxue and Costello, Joseph F and Wang, Ting}, 
title = {Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm},
year = {2013}, 
doi = {10.1101/gr.156539.113}, 
elocation-id = {gr.156539.113}, 
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.}, 
URL = {http://genome.cshlp.org/content/early/2013/06/26/gr.156539.113.abstract}, 
eprint = {http://genome.cshlp.org/content/early/2013/06/26/gr.156539.113.full.pdf+html}, 
journal = {Genome Research} 
}