dna fragments were cleaned - up , end - repaired , a - tailed , and ligated with methylated paired - end adapters ( purchased from atdbio ) .
see supplementary information for details . in house generated wgbs libraries were aligned using maq in bisulfite mode to the hg19/grch37 reference assembly .
subsequently , cpg methylation calls were made using custom software , excluding duplicate , low quality reads as well as reads with more than 10% mismatches .
methylation ratios of individual cpgs were modeled using a beta - binomial model estimating parameters from the number of methylated and total reads overlapping a given cpg , incorporating replicates . only cpgs covered by 5 reads were considered for further analysis .
cpg cluster differential methylation was determined by pooling cpg level methylation differences using a random effects model .
cpg cluster methylation specificity was determined using the jensen - shannon divergence of a cpg cluster s methylation level distribution across all samples and a reference distribution representing either of the two extremes : completely unmethylated or fully methylated . in silico
identified cpg islands were defined by genomic regions of at least 700bp length , an cpg observed vs. expected ratio of greater than 0.6 and a gc content greater or equal than 0.5 . for the snp analysis , we obtained the ceph snp set from uscs .
gwas snps were retrieved from the gwas catalog , while most of the gwas snp grouping was taken from maurano et al .. for tfbs analysis , we retrieved peak files from the encode projects and collapsed replicates .
dna fragments were cleaned - up , end - repaired , a - tailed , and ligated with methylated paired - end adapters ( purchased from atdbio ) .
in house generated wgbs libraries were aligned using maq in bisulfite mode to the hg19/grch37 reference assembly .
subsequently , cpg methylation calls were made using custom software , excluding duplicate , low quality reads as well as reads with more than 10% mismatches .
methylation ratios of individual cpgs were modeled using a beta - binomial model estimating parameters from the number of methylated and total reads overlapping a given cpg , incorporating replicates . only cpgs covered by 5 reads were considered for further analysis .
cpg cluster differential methylation was determined by pooling cpg level methylation differences using a random effects model .
cpg cluster methylation specificity was determined using the jensen - shannon divergence of a cpg cluster s methylation level distribution across all samples and a reference distribution representing either of the two extremes : completely unmethylated or fully methylated . in silico
identified cpg islands were defined by genomic regions of at least 700bp length , an cpg observed vs. expected ratio of greater than 0.6 and a gc content greater or equal than 0.5 . for the snp analysis
gwas snps were retrieved from the gwas catalog , while most of the gwas snp grouping was taken from maurano et al .. for tfbs analysis , we retrieved peak files from the encode projects and collapsed replicates .
 dna methylation is a defining feature of mammalian cellular identity and essential for normal development1,2 . 
 most cell types , except germ cells and pre - implantation embryos35 , display relatively stable dna methylation patterns with 7080% of all cpgs being methylated6 . despite recent advances we still have a too limited understanding of when , where and how many cpgs participate in genomic regulation . here 
 we report the in depth analysis of 42 whole genome bisulfite sequencing ( wgbs ) data sets across 30 diverse human cell and tissue types . 
 we observe dynamic regulation for only 21.8% of autosomal cpgs within a normal developmental context , a majority of which are distal to transcription start sites . 
 these dynamic cpgs co - localize with gene regulatory elements , particularly enhancers and transcription factor binding sites ( tfbs ) , which allow identification of key lineage specific regulators . 
 in addition , differentially methylated regions ( dmrs ) often harbor snps associated with cell type related diseases as determined by gwas . 
 the results also highlight the general inefficiency of wgbs as 7080% of the sequencing reads across these data sets provided little or no relevant information regarding cpg methylation . to further demonstrate the utility of our dmr set , we use it to classify unknown samples and identify representative signature regions that recapitulate major dna methylation dynamics . in summary , although in theory every cpg can change its methylation state , our results suggest that only a fraction does so as part of coordinated regulatory programs 
 . therefore our selected dmrs can serve as a starting point to help guide novel , more effective reduced representation approaches to capture the most informative fraction of cpgs as well as further pinpoint putative regulatory elements . 
Methods Summary
Biological materials and sequencing libraries
Data processing and analysis
Supplementary Material