RT Journal
A1 Zhang, Bo
A1 Zhou, Yan
A1 Lin, Nan
A1 Lowdon, Rebecca F.
A1 Hong, Chibo
A1 Nagarajan, Raman P.
A1 Cheng, Jeffrey B.
A1 Li, Daofeng
A1 Stevens, Michael
A1 Lee, Hyung Joo
A1 Xing, Xiaoyun
A1 Zhou, Jia
A1 Sundaram, Vasavi
A1 Elliott, GiNell
A1 Gu, Junchen
A1 Shi, Taoping
A1 Gascard, Philippe
A1 Sigaroudinia, Mahvash
A1 Tlsty, Thea D.
A1 Kadlecek, Theresa
A1 Weiss, Arthur
A1 O’Geen, Henriette
A1 Farnham, Peggy J.
A1 Maire, Cécile L.
A1 Ligon, Keith L.
A1 Madden, Pamela A.F.
A1 Tam, Angela
A1 Moore, Richard
A1 Hirst, Martin
A1 Marra, Marco A.
A1 Zhang, Baoxue
A1 Costello, Joseph F.
A1 Wang, Ting
T1 Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm
JF Genome Research 
JO Genome Research 
YR 2013 
FD September 01 
VO 23 
IS 9 
SP 1522 
OP 1540 
DO 10.1101/gr.156539.113 
UL http://genome.cshlp.org/content/23/9/1522.abstract 
AB DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. This includes one of the most widely applied technologies for measuring DNA methylation: methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been unavailable. 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.