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      Identification of differentially methylated loci using wavelet-based functional mixed models

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      Bioinformatics

      Oxford University Press

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          Abstract

          Motivation: DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation.

          Results: We applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study.

          Availability and implementation: Automated software called WFMM is available at https://biostatistics.mdanderson.org/SoftwareDownload. CpG Shore data is available at http://rafalab.dfci.harvard.edu. NIH Roadmap Epigenomics data is available at http://compbio.mit.edu/roadmap.

          Supplementary information: Supplementary data are available at Bioinformatics online.

          Contact: jefmorris@ 123456mdanderson.org

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          Author and article information

          Journal
          Bioinformatics
          Bioinformatics
          bioinformatics
          bioinfo
          Bioinformatics
          Oxford University Press
          1367-4803
          1367-4811
          01 March 2016
          11 November 2015
          : 32
          : 5
          : 664-672
          Affiliations
          Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
          Author notes
          *To whom correspondence should be addressed.

          Associate Editor: John Hancock

          Article
          PMC4907398 PMC4907398 4907398 btv659
          10.1093/bioinformatics/btv659
          4907398
          26559505
          © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
          Page count
          Pages: 9
          Categories
          Original Papers
          Genome Analysis

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