Analysis abstract
Nature Biotechnology 26, 779 - 785 (2008)
Published online: 8 July 2008 | doi:10.1038/nbt1414
A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis
Thomas A Down1,8, Vardhman K Rakyan2,8, Daniel J Turner3, Paul Flicek4, Heng Li3, Eugene Kulesha4, Stefan Gräf4, Nathan Johnson4, Javier Herrero4, Eleni M Tomazou3, Natalie P Thorne5, Liselotte Bäckdahl6, Marlis Herberth7, Kevin L Howe5, David K Jackson3, Marcos M Miretti3, John C Marioni5, Ewan Birney4, Tim J P Hubbard3, Richard Durbin3, Simon Tavaré5 & Stephan Beck6
Abstract
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.
- Wellcome Trust Cancer Research UK Gurdon Institute, and Department of Genetics, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK.
- Institute of Cell and Molecular Science, Barts and The London, 4 Newark Street, London, E1 2AT, UK.
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK.
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK.
- Department of Oncology, University of Cambridge, Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.
- UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
- Institute of Biotechnology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QT, UK.
- These authors contributed equally to this work.
Correspondence to: Vardhman K Rakyan2,8 e-mail: v.rakyan@qmul.ac.uk
Correspondence to: Thomas A Down1,8 e-mail: thomas.down@gurdon.cam.ac.uk
Correspondence to: Stephan Beck6 e-mail: s.beck@ucl.ac.uk
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