TY  - JOUR
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  - 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, Cecile L
A1  - Ligon, Keith L
A1  - Madden, Pamela AF
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
Y1  - 2013/06/26 
JF  - Genome Research 
JO  - Genome Research 
DO  - 10.1101/gr.156539.113 
SP  - gr.156539.113 
UR  - http://genome.cshlp.org/content/early/2013/06/26/gr.156539.113.abstract 
N2  - 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. 
ER  -