title: seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data.
abstract: One of the main goals of large scale methylation studies is to detect differentially methylated loci. One way is to approach this problem sitewise, i.e. to find differentially methylated positions (DMPs). However, it has been shown that methylation is regulated in longer genomic regions. So it is more desirable to identify differentially methylated regions (DMRs) instead of DMPs. The new high coverage arrays, like Illuminas 450k platform, make it possible at a reasonable cost. Few tools exist for DMR identification from this type of data, but there is no standard approach.
  
  
  
  We propose a novel method for DMR identification that detects the region boundaries according to the minimum description length (MDL) principle, essentially solving the problem of model selection. The significance of the regions is established using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches.
  
  
  
  The methods have been implemented in R package seqlm that is available through Github: https://github.com/raivokolde/seqlm
  
  
  
  rkolde@gmail.com
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
authors.forename: Raivo
authors.initials: R
authors.lastname: Kolde
authors.affiliation: Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.
authors.forename: Kaspar
authors.initials: K
authors.lastname: Märtens
authors.affiliation: Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia.
authors.forename: Kaie
authors.initials: K
authors.lastname: Lokk
authors.affiliation: Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.
authors.forename: Sven
authors.initials: S
authors.lastname: Laur
authors.affiliation: Institute of Computer Science, University of Tartu, 50409 Tartu, Estonia.
authors.forename: Jaak
authors.initials: J
authors.lastname: Vilo
mesh_terms: D019175:DNA Methylation; D066264:Datasets as Topic; D016678:Genome; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing