title: Efficient detection of differentially methylated regions using DiMmeR.
abstract: Epigenome-wide association studies (EWAS) generate big epidemiological datasets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes.
  
  
  
  Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical P -values through randomization tests, even for big datasets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, P -values and empirical P -values, and it corrects for multiple testing.
  
  
  
  DiMmeR is publicly available at http://dimmer.compbio.sdu.dk .
  
  
  
  diogoma@bmb.sdu.dk.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark.|Laboratory for Genomics and Bioinformatics, Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil.
authors: Diogo
authors: D
authors: Almeida
authors: Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.
authors: Ida
authors: I
authors: Skov
authors: Laboratory for Genomics and Bioinformatics, Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil.
authors: Artur
authors: A
authors: Silva
authors: Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.|Department of Information Engineering, University of Padova, Padova I-35131, Italy.
authors: Fabio
authors: F
authors: Vandin
authors: Clinical Institute (CI), University of Southern Denmark, 5000 Odense, Denmark.
authors: Qihua
authors: Q
authors: Tan
authors: Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.
authors: Richard
authors: R
authors: Röttger
authors: Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.|Max Planck Institute for Informatics, Saarbruecken, Germany.
authors: Jan
authors: J
authors: Baumbach
mesh_terms: D018899:CpG Islands; D019175:DNA Methylation; D057890:Epigenomics / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software