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This article is part of a special issue on epigenomics.

Open Access Highly Accessed Software

methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles

Altuna Akalin12*, Matthias Kormaksson3, Sheng Li12, Francine E Garrett-Bakelman4, Maria E Figueroa5, Ari Melnick46 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 Public Health, Weill Cornell Medical College, 1300 York Ave., New York, NY 10065, USA

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

5 Department of Pathology, University of Michigan, 109 Zina Pitcher Place, Ann Arbor, MI 48109, USA

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

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Genome Biology 2012, 13:R87  doi:10.1186/gb-2012-13-10-r87

Published: 3 October 2012

Abstract

DNA methylation is a chemical modification of cytosine bases that is pivotal for gene regulation, cellular specification and cancer development. Here, we describe an R package, methylKit, that rapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation and hydroxymethylation sequencing experiments. methylKit includes functions for clustering, sample quality visualization, differential methylation analysis and annotation features, thus automating and simplifying many of the steps for discerning statistically significant bases or regions of DNA methylation. Finally, we demonstrate methylKit on breast cancer data, in which we find statistically significant regions of differential methylation and stratify tumor subtypes. methylKit is available at http://code.google.com/p/methylkit webcite.