title: MeDIP-HMM: genome-wide identification of distinct DNA methylation states from high-density tiling arrays.
abstract: BACKGROUND
  Methylation of cytosines in DNA is an important epigenetic mechanism involved in transcriptional regulation and preservation of genome integrity in a wide range of eukaryotes. Immunoprecipitation of methylated DNA followed by hybridization to genomic tiling arrays (MeDIP-chip) is a cost-effective and sensitive method for methylome analyses. However, existing bioinformatics methods only enable a binary classification into unmethylated and methylated genomic regions, which limit biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number and degree of methylated cytosines. Therefore, a method for the identification of more than two methylation states is highly desirable.
  
  
  RESULTS
  Here, we present a three-state hidden Markov model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM uses a higher-order state-transition process improving modeling of spatial dependencies between chromosomal regions, allows a simultaneous analysis of replicates and enables a differentiation between unmethylated, methylated and highly methylated genomic regions. We train MeDIP-HMM using a Bayesian Baum-Welch algorithm, integrating prior knowledge on methylation levels. We apply MeDIP-HMM to the analysis of the Arabidopsis root methylome and systematically investigate the benefit of using higher-order HMMs. Moreover, we also perform an in-depth comparison study with existing methods and demonstrate the value of using MeDIP-HMM by comparisons to current knowledge on the Arabidopsis methylome. We find that MeDIP-HMM is a fast and precise method for the analysis of methylome data, enabling the identification of distinct DNA methylation levels. Finally, we provide evidence for the general applicability of MeDIP-HMM by analyzing promoter DNA methylation data obtained for chicken.
  
  
  BACKGROUND
  MeDIP-HMM is available as part of the open-source Java library Jstacs (www.jstacs.de/index.php/MeDIP-HMM). Data files are available from the Jstacs website.
  
  
  BACKGROUND
  seifert@ipk-gatersleben.de.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, Gatersleben, Germany. seifert@ipk-gatersleben.de
authors.forename: Michael
authors.initials: M
authors.lastname: Seifert
authors.forename: Sandra
authors.initials: S
authors.lastname: Cortijo
authors.forename: Maria
authors.initials: M
authors.lastname: Colomé-Tatché
authors.forename: Frank
authors.initials: F
authors.lastname: Johannes
authors.forename: François
authors.initials: F
authors.lastname: Roudier
authors.forename: Vincent
authors.initials: V
authors.lastname: Colot
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D001499:Bayes Theorem; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D047468:Immunoprecipitation / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods