Nucleic Acids Research Advance Access originally published online on October 13, 2006
Nucleic Acids Research 2006 34(20):e136; doi:10.1093/nar/gkl551
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Nucleic Acids Research, 2006, Vol. 34, No. 20 e136
© 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.
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MMASS: an optimized array-based method for assessing CpG island methylation
1 Department of Pathology, Division of Molecular Histopathology, Addenbrooke's Hospital Hills Road, Cambridge CB2 2XZ, UK 2 Cancer Genomics Program, Department of Oncology, Hutchison/MRC Research Centre Hills Road, Cambridge CB2 2XZ, UK 3 Department of Oncology Hills Road, Cambridge CB2 2XZ, UK 4 Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Hutchison/MRC Research Centre Hills Road, Cambridge CB2 2XZ, UK 5 Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon Avenue Prof. Egas Moniz, 1649–028 Lisboa, Portugal
*To whom correspondence should be addressed. Tel: +44 1223 256295; Fax: +44 1223 586670; Email: aeki2{at}cam.ac.uk
Received January 17, 2006. Revised May 10, 2006. Accepted July 14, 2006.
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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