MeDIP-HMM: Difference between revisions
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== Description == | == Description == | ||
=== Motivation === | === 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 limits biological interpretations. Indeed, DNA methylation levels can vary substantially within a given DNA fragment depending on the number of | 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. | ||
=== Results === | === Results === | ||
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. | 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. | ||
== Paper == | == Paper == | ||
The paper '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays''''' has been | The paper [http://bioinformatics.oxfordjournals.org/content/early/2012/09/17/bioinformatics.bts562.abstract '''''MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays'''''] has been published in [http://bioinformatics.oxfordjournals.org/ Bioinformatics]. | ||
== Download == | == Download == | ||
* [ | * [https://imbcloud.medizin.tu-dresden.de/sharing/iWr3Uix4A 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. | ||
== Related Projects == | |||
* [[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 | |||
* [https://sites.google.com/site/mseifertweb/hmm-book HMM Book]: Hidden Markov Models with Applications in Computational Biology | |||
== Follow Me == | |||
* [https://sites.google.com/site/michaelseiferthmm/home Personal Homepage] | |||
Latest revision as of 08:11, 24 March 2021
by Michael Seifert, Sandra Cortijo, Maria Colome-Tatche, Frank Johannes, Francois Roudier, and Vincent Colot
Description
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 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.
Results
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.
Paper
The paper MeDIP-HMM: Genome-wide identification of distinct DNA methylation states from high-density tiling arrays has been published in Bioinformatics.
Download
- 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.
Related Projects
- 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
