by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot
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 limits 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.
Here, we present a three-state Hidden Markov Model (MeDIP-HMM) for analyzing MeDIP-chip data. MeDIP-HMM utilizes 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 to 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 DNA methylation 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.
The paper MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays has been published in Bioinformatics.
- MeDIP-HMM: A ZIP file containing a JAR file for analyzing methylome data sets by MeDIP-HMMs. This file also contains the Arabidopsis root methylome and the chicken data sets considered in our study.
- ARHMM: integrating local chromosomal dependencies into the analysis of tumor expression profiles
- DSHMM: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles
- SHMM: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data
- PHHMM: improved analysis of Array-CGH data
- HMM Book: Hidden Markov Models with Applications in Computational Biology