This article is part of the supplement: Proceedings of the Third Annual RECOMB Satellite Workshop on Massively Parallel Sequencing (RECOMB-seq 2013)

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An optimized algorithm for detecting and annotating regional differential methylation

Sheng Li12, Francine E Garrett-Bakelman3, Altuna Akalin12, Paul Zumbo12, Ross Levine4, Bik L To5, Ian D Lewis5, Anna L Brown6, Richard J D'Andrea67, Ari Melnick38 and Christopher E Mason12*

Author Affiliations

1 Department of Physiology and Biophysics, 1305 York Ave., Weill Cornell Medical College, New York, NY 10065, USA

2 The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, 1305 York Ave., Weill Cornell Medical College, New York, NY 10065, USA

3 Department of Medicine, Division of Hematology/Oncology, 1300 York Ave., Weill Cornell Medical College, New York, NY 10065, USA

4 Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 20, New York, NY 10065, USA

5 Directorate of Haematology, SA Pathology and Department of Haematology, Royal Adelaide Hospital, Adelaide, South Australia

6 Directorate of Haematology and Centre for Cancer Biology SA Pathology, The Queen Elizabeth Hospital, Woodville, South Australia

7 Department of Haematology and Oncology, The Queen Elizabeth Hospital, Woodville, South Australia

8 Department of Pharmacology, 1300 York Ave., Weill Cornell Medical College, New York, NY 10065, USA

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BMC Bioinformatics 2013, 14(Suppl 5):S10  doi:10.1186/1471-2105-14-S5-S10

Published: 10 April 2013



DNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data.


Here we show that our bimodal distribution model and weighted cost function for optimized regional methylation analysis provides accurate boundaries of regions harboring significant epigenetic modifications. Our algorithm takes the spatial distribution of CpGs into account for the enrichment assay, allowing for optimization of the definition of empirical regions for differential methylation. Combined with the dependent adjustment for regional p-value combination and DMR annotation, we provide a method that may be applied to a variety of datasets for rapid DMR analysis. Our method classifies both the directionality of DMRs and their genome-wide distribution, and we have observed that shows clinical relevance through correct stratification of two Acute Myeloid Leukemia (AML) tumor sub-types.


Our weighted optimization algorithm eDMR for calling DMRs extends an established DMR R pipeline (methylKit) and provides a needed resource in epigenomics. Our method enables an accurate and scalable way of finding DMRs in high-throughput methylation sequencing experiments. eDMR is available for download at webcite.