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Computational analysis of genome-wide DNA-methylation during the differentiation of human embryonic stem cells along the endodermal lineage

ABSTRACT: The generation of genome-wide data derived from methylated DNA immunoprecipitation followed by sequencing (MeDIP-Seq) has become a major tool for epigenetic studies in health and disease. The computational analysis of such data, however, still falls short on accuracy, sensitivity and speed. We propose a statistical method that is able to cope with the inherent complexity of MeDIP-Seq data and outperforms computation time of existing methods by orders of magnitude with similar performance. In order to demonstrate the computational approach, we have analysed alterations in DNA methylation during the differentiation of hESCs to definitive endoderm. We show improved correlation of normalized MeDIP-Seq data in comparison to available whole-genome bisulphite sequencing data and investigated the effect of differential methylation on gene expression. Furthermore, we analyzed the interplay between DNA-methylation, histone modifications, transcription factor binding, and show that in contrast to de-novo methylation, de-methylation is mainly associated with regions of low CpG densities.

ORGANISM(S): Homo sapiens

PROVIDER: GSE21715 | GEO | 2010/07/27

SECONDARY ACCESSION(S): PRJNA127153

REPOSITORIES: GEO

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Project description:Genome-wide DNA methylation profile of human spermatozoa using the meDIP technique and massively parallel sequencing. DNA methylation is an indispensible epigenetic modification required for regulating the expression of mammalian genomes. Immunoprecipitation-based methods for DNA methylome analysis are rapidly shifting the bottleneck in this field from data generation to data analysis, necessitating the development of better analytical tools. In particular, an inability to estimate absolute methylation levels remains a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling. To address this issue, we developed a cross-platform algorithm-Bayesian tool for methylation analysis (Batman)-for analyzing methylated DNA immunoprecipitation (MeDIP) profiles generated using oligonucleotide arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). We developed the latter approach to provide a high-resolution whole-genome DNA methylation profile (DNA methylome) of a mammalian genome. Strong correlation of our data, obtained using mature human spermatozoa, with those obtained using bisulfite sequencing suggest that combining MeDIP-seq or MeDIP-chip with Batman provides a robust, quantitative and cost-effective functional genomic strategy for elucidating the function of DNA methylation. ArrayExpress Release Date: 2008-06-10 Publication Title: A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis Publication Author List: Thomas A Down; Vardhman K Rakyan; Daniel J Turner; Paul Flicek; Heng Li; Eugene Kulesha; Stefan GrÃ¤f; Nathan Johnson; Javier Herrero; Eleni M Tomazou; Natalie P Thorne; Liselotte BÃ¤ckdahl; Marlis Herberth; Kevin L Howe; David K Jackson; Marcos M Miretti; John C Marioni; Ewan Birney; Tim J P Hubbard; Richard Durbin; Simon TavarÃ©; Stephan Beck Person Roles: submitter; investigator Person Last Name: Flicek Person First Name: Paul Person Mid Initial Person Email: flicek@ebi.ac.uk Person Address: Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK Person Affiliation: European Bioinformatics Institute

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Computational analysis of genome-wide DNA-methylation during the differentiation of human embryonic stem cells along the endodermal lineage

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