Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/82250
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Type: | Journal article |
Title: | An optimized algorithm for detecting and annotating regional differential methylation |
Author: | Li, S. Garrett-Bakelman, F. Akalin, A. Zumbo, P. Levine, R. To, L. Lewis, I. Brown, A. D'Andrea, R. Melnick, A. Mason, C. |
Citation: | BMC Bioinformatics, 2013; 14(supp 5):1-9 |
Publisher: | BioMed Central Ltd. |
Issue Date: | 2013 |
ISSN: | 1471-2105 1471-2105 |
Statement of Responsibility: | Sheng Li, Francine E Garrett-Bakelman, Altuna Akalin, Paul Zumbo, Ross Levine, Bik L To, Ian D Lewis, Anna L Brown, Richard J D’Andrea, Ari Melnick, Christopher E Mason |
Abstract: | Background: 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. Results: 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. Conclusions: 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 http://code.google.com/p/edmr/. |
Keywords: | Humans Leukemia Sequence Analysis, DNA Genomics DNA Methylation CpG Islands Genome Algorithms Epigenomics Molecular Sequence Annotation High-Throughput Nucleotide Sequencing |
Rights: | © 2013 Li et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
DOI: | 10.1186/1471-2105-14-S5-S10 |
Published version: | http://dx.doi.org/10.1186/1471-2105-14-s5-s10 |
Appears in Collections: | Aurora harvest Medical Sciences publications |
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hdl_82250.pdf | Published version | 1.08 MB | Adobe PDF | View/Open |
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