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While there are many publications exploring the biology of DNA methylation and the epigenome, and a large number of articles describing the development of approaches for studying DNA methylation, there are few articles that address the analytic issues involved in these experiments. We have reviewed
the technologies and analysis steps used in methylation array experiments, in a way accessible to both biologists and bioinformaticians
In collaboration with the Beck lab at WTSI we have looked at the problem of analysing patterns of CpG methylation. 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. Estimating absolute methylation levels presents a major problem for such technologies. To address
this issue, we developed a cross-platform algorithm called “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). The latter
approach was used to provide a high-resolution whole-genome DNA methylation profile (DNA
methylome) of a mammalian genome. We showed 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.
We have described a novel resource (mPod) for human genome-wide tissue-specific DNA methylation
profiles. mPod consists of three fully integrated parts, genome-wide DNA methylation reference
profiles of 13 normal somatic tissues, placenta, sperm, and an immortalised cell line, a
visualization tool that has been integrated with the Ensembl genome browser and a new algorithm
for the analysis of immunoprecipitation-based DNA methylation profiles. We demonstrated
the utility of our resource by identifying the first comprehensive genome-wide set of tissue-specific
differentially methylated regions (tDMRs) that may play a role in cellular identity and the regulation
of tissue-specific genome function. We also discussed the implications of our findings with respect
to the regulatory potential of regions with varied CpG density, gene expression, transcription factor
motifs, gene ontology, and correlation with other epigenetic marks such as histone modifications.
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| Selected publications of ours in this area |
- Thorne N, Marioni J, Rakyan V, Ibrahim A, Massie C, Curtis C, Brenton J, Murrell A, and
Tavare S DNA methylation arrays: Methods and analysis. In Microarray Innovations: Technology
and Experimentation in Drug Discovery and Biomedical Research 2009 G. Hardiman, ed., Ch. 13. (CRC
Press/Taylor and Francis).
- Down TA, Rakyan VK, Turner DJ, Flicek P, Li H, Kulesha E, Gräf S, Johnson N, Herrero J, Tomazou EM, Thorne NP, Bäckdahl L, Herberth M, Howe KL, Jackson DK, Miretti MM, Marioni JC, Birney E, Hubbard TJ, Durbin R, Tavaré S, Beck S. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis Nat Biotechnol 2008 Jul;26(7):779-85 [pubmed]
- Rakyan VK, Down TA, Thorne NP, Flicek P, Kulesha E, Gräf S, Tomazou EM, Bäckdahl L, Johnson N, Herberth M, Howe KL, Jackson DK, Miretti MM, Fiegler H, Marioni JC, Birney E, Hubbard TJ, Carter NP, Tavaré S, Beck S. An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs) Genome Res 2008 Sep;18(9):1518-29 [pubmed]
- Ibrahim AE, Thorne NP, Baird K, Barbosa-Morais NL, Tavaré S, Collins VP, Wyllie AH, Arends MJ, Brenton JD. MMASS: an optimised array-based method for assessing CpG island methylation. Nucleic Acids Res. 2006;34(20):e136. Epub 2006 Oct 13 [pubmed]
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