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      MMASS: an optimized array-based method for assessing CpG island methylation

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          Abstract

          We describe an optimized microarray method for identifying genome-wide CpG island methylation called microarray-based methylation assessment of single samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimized combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared with a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison with previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation.

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          Most cited references46

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          The Bioperl toolkit: Perl modules for the life sciences.

          The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.
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            Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells.

            Cytosine methylation is required for mammalian development and is often perturbed in human cancer. To determine how this epigenetic modification is distributed in the genomes of primary and transformed cells, we used an immunocapturing approach followed by DNA microarray analysis to generate methylation profiles of all human chromosomes at 80-kb resolution and for a large set of CpG islands. In primary cells we identified broad genomic regions of differential methylation with higher levels in gene-rich neighborhoods. Female and male cells had indistinguishable profiles for autosomes but differences on the X chromosome. The inactive X chromosome (Xi) was hypermethylated at only a subset of gene-rich regions and, unexpectedly, overall hypomethylated relative to its active counterpart. The chromosomal methylation profile of transformed cells was similar to that of primary cells. Nevertheless, we detected large genomic segments with hypomethylation in the transformed cell residing in gene-poor areas. Furthermore, analysis of 6,000 CpG islands showed that only a small set of promoters was methylated differentially, suggesting that aberrant methylation of CpG island promoters in malignancy might be less frequent than previously hypothesized.
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              Cancer epigenetics comes of age.

              The discovery of numerous hypermethylated promoters of tumour-suppressor genes, along with a better understanding of gene-silencing mechanisms, has moved DNA methylation from obscurity to recognition as an alternative mechanism of tumour-suppressor inactivation in cancer. Epigenetic events can also facilitate genetic damage, as illustrated by the increased mutagenicity of 5-methylcytosine and the silencing of the MLH1 mismatch repair gene by DNA methylation in colorectal tumours. We review here current mechanistic understanding of the role of DNA methylation in malignant transformation, and suggest Knudson's two-hit hypothesis should now be expanded to include epigenetic mechanisms of gene inactivation.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Research
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                November 2006
                November 2006
                13 November 2006
                : 34
                : 20
                : e136
                Affiliations
                1Department of Pathology, Division of Molecular Histopathology, Addenbrooke's Hospital Hills Road, Cambridge CB2 2XZ, UK
                2Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Centre Hills Road, Cambridge CB2 2XZ, UK
                3Department of Oncology Hills Road, Cambridge CB2 2XZ, UK
                4Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Hutchison/MRC Research Centre Hills Road, Cambridge CB2 2XZ, UK
                5Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon Avenue Prof. Egas Moniz, 1649–028 Lisboa, Portugal
                Author notes
                *To whom correspondence should be addressed. Tel: +44 1223 256295; Fax: +44 1223 586670; Email: aeki2@ 123456cam.ac.uk
                Article
                10.1093/nar/gkl551
                1635254
                17041235
                261cfbe4-95a9-4387-8ac1-90c9735bd2f3
                © 2006 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 January 2006
                : 10 May 2006
                : 14 July 2006
                Categories
                Methods Online

                Genetics
                Genetics

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