MOTIVATION: 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. AVAILABILITY: 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. CONTACT: seifert@ipk-gatersleben.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. AVAILABILITY: 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. CONTACT: seifert@ipk-gatersleben.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Michael Seifert; Khalil Abou-El-Ardat; Betty Friedrich; Barbara Klink; Andreas Deutsch Journal: PLoS One Date: 2014-06-23 Impact factor: 3.240
Authors: Jörg Hagmann; Claude Becker; Jonas Müller; Oliver Stegle; Rhonda C Meyer; George Wang; Korbinian Schneeberger; Joffrey Fitz; Thomas Altmann; Joy Bergelson; Karsten Borgwardt; Detlef Weigel Journal: PLoS Genet Date: 2015-01-08 Impact factor: 5.917
Authors: John P Thomson; Jennifer M Hunter; Harri Lempiäinen; Arne Müller; Rémi Terranova; Jonathan G Moggs; Richard R Meehan Journal: Nucleic Acids Res Date: 2013-04-17 Impact factor: 16.971