Advanced, high-throughput sequencing technologies allow for fast, single-base resolution scans of entire epigenome. Large-scale sequencing projects are producing these datasets for cancer research, and these epigenetic marks provide important information about cellular phenotypes in normal and diseased tissues [1, 2]. DNA methylation pattern changes are pivotal marks needed in cells' differentiation during tissue and lineage specification, and, as such, contribute to the complexity of organisms' cellular sub-types [3, 4]. Furthermore, aberrant DNA methylation not only defines malignant subtypes of disease [5, 6], but also contributes to malignant disease pathophysiology and can be used in clinical outcome predictions .
Bisulfite sequencing of genomic DNA is a widely applied method for methylation measurement. Whole-genome bisulfite sequencing is a genome-wide technique for the measurement of DNA methylation . However, other enrichment DNA methylation sequencing methods have been developed to achieve cost-effective coverage of variable regions of DNA methylation. These methods often utilize reduced representation of bisulfite sequencing by focusing on restriction sites, including methods such as Reduced Representation Bisulfite sequencing (RRBS) [9–11], Enhanced RRBS (ERRBS) , multiplexed RRBS , methylation-sensitive restriction enzyme sequencing , as well as other enrichment approaches, including methylated DNA immunoprecipitation sequencing  and methylated DNA binding domain sequencing .
Previously, epigenome analysis tools such as methylKit  have focused on comprehensive DNA methylation analysis of single base sites, in order to find differentially methylated cytosines (DMCs). However, biological regulation by methylation can be mediated by a single CpG or by a group of CpGs in close proximity to each other. Therefore, a combination of baseresolution analysis and regional analysis of DNA methylation may offer a more comprehensive and systematic view of bisulfate sequencing data. This increasing demand for tools to find differentially methylated regions (DMRs) increases as more data emerge from both large-scale epigenomics consortiums and from individual labs. To address this need, we have created eDMR, which exists as stand-alone code for use with other tools and packages. It can also be used as an expansion of the methylKit R package for comprehensive DMR analysis. eDMR can directly take objects from methylKit or data frames with differential methylation information, or any DMC result in bed file format, and perform regional optimization calling and DMR statistical analysis and filtering. Furthermore, eDMR offers annotation functions that map DMRs to gene body features (coding sequences, introns, promoters, 5' untranslated regions (UTR), and 3'UTR), CpG island and shore locations, as well as the use of any other user-supplied coordinate files for annotation. Here, we describe an example of using eDMR with DNA methylation data from the ERRBS protocol.