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

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 time-efficient statistical method that is able to cope with the inherent complexity of MeDIP-seq data with similar performance compared with existing methods. In order to demonstrate the computational approach, we have analyzed alterations in DNA methylation during the differentiation of human embryonic stem cells (hESCs) to definitive endoderm. We show improved correlation of normalized MeDIP-seq data in comparison to available whole-genome bisulfite sequencing data, and investigated the effect of differential methylation on gene expression. Furthermore, we analyzed the interplay between DNA methylation, histone modifications, and transcription factor binding and show that in contrast to de novo methylation, demethylation is mainly associated with regions of low CpG densities.

[1]  Daiya Takai,et al.  Comprehensive analysis of CpG islands in human chromosomes 21 and 22 , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[2]  A. Feinberg,et al.  Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores , 2008, Nature Genetics.

[3]  H. Lehrach,et al.  In silico identification of a core regulatory network of OCT4 in human embryonic stem cells using an integrated approach , 2009, BMC Genomics.

[4]  Ting Wang,et al.  The UCSC Genome Browser Database: update 2009 , 2008, Nucleic Acids Res..

[5]  Dustin E. Schones,et al.  High-Resolution Profiling of Histone Methylations in the Human Genome , 2007, Cell.

[6]  Louise C Laurent,et al.  DNA methylation in embryonic stem cells , 2009, Journal of cellular biochemistry.

[7]  Gordon K. Smyth,et al.  limmaGUI: A graphical user interface for linear modeling of microarray data , 2004, Bioinform..

[8]  A. Urban,et al.  MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. , 2008, Genome research.

[9]  Timothy E. Reddy,et al.  Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. , 2009, Genome research.

[10]  Joachim Büch,et al.  EpiGRAPH: user-friendly software for statistical analysis and prediction of (epi)genomic data , 2009, Genome Biology.

[11]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[12]  R. Lister,et al.  Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis , 2008, Cell.

[13]  David Haussler,et al.  The UCSC Genome Browser database: update 2010 , 2009, Nucleic Acids Res..

[14]  E. Kroon,et al.  Efficient differentiation of human embryonic stem cells to definitive endoderm , 2005, Nature Biotechnology.

[15]  E. Birney,et al.  An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). , 2008, Genome research.

[16]  Simon Tavaré,et al.  beadarray: R classes and methods for Illumina bead-based data , 2007, Bioinform..

[17]  G. Daley,et al.  Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming , 2009, Nature Biotechnology.

[18]  Mary Goldman,et al.  The UCSC Genome Browser database: update 2011 , 2010, Nucleic Acids Res..

[19]  Peter A. Jones,et al.  The Epigenomics of Cancer , 2007, Cell.

[20]  Simon C. Potter,et al.  An overview of Ensembl. , 2004, Genome research.

[21]  T. Mikkelsen,et al.  Genome-scale DNA methylation maps of pluripotent and differentiated cells , 2008, Nature.

[22]  Lee E. Edsall,et al.  Human DNA methylomes at base resolution show widespread epigenomic differences , 2009, Nature.

[23]  Robert Lanza,et al.  Efficient Differentiation of Functional Hepatocytes from Human Embryonic Stem Cells , 2008, Stem cells.

[24]  Tatiana Tatusova,et al.  NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins , 2004, Nucleic Acids Res..

[25]  W. Lam,et al.  Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells , 2005, Nature Genetics.

[26]  R. Durbin,et al.  A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis , 2008, Nature Biotechnology.

[27]  S. Ariyan,et al.  Genome-wide screen of promoter methylation identifies novel markers in melanoma. , 2009, Genome research.

[28]  M. Frommer,et al.  CpG islands in vertebrate genomes. , 1987, Journal of molecular biology.

[29]  P. Laird.  Principles and challenges of genome-wide DNA methylation analysis , 2010, Nature Reviews Genetics.

[30]  J. Rogers,et al.  DNA methylation profiling of human chromosomes 6, 20 and 22 , 2006, Nature Genetics.

[31]  G. Daley,et al.  Live cell imaging distinguishes bona fide human iPS cells from partially reprogrammed cells , 2009, Nature Biotechnology.

[32]  R. Durbin,et al.  Mapping Quality Scores Mapping Short Dna Sequencing Reads and Calling Variants Using P

, 2022 .

[33]  T. Borodina,et al.  Transcriptome analysis by strand-specific sequencing of complementary DNA , 2009, Nucleic acids research.