Differential methylation hybridization: profiling DNA methylation with a high-density CpG island microarray

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 507; p. 89
• Main Authors: Yan, Pearlly S, Potter, Dustin, Deatherage, Daniel E, Huang, Tim H-M, Lin, Shili
• Format: Journal Article
• Language: English
• Published: United States 2009
• Subjects: Base Sequence; Biomarkers, Tumor - chemistry; Biomarkers, Tumor - genetics; CpG Islands; Data Interpretation, Statistical; DNA - chemistry; DNA - genetics; DNA Fingerprinting - methods; DNA Fingerprinting - statistics & numerical data; DNA Methylation; DNA, Neoplasm - chemistry; DNA, Neoplasm - genetics; Humans; Neoplasms - chemistry; Neoplasms - genetics; Nucleic Acid Amplification Techniques; Nucleic Acid Hybridization; Oligonucleotide Array Sequence Analysis - methods; Oligonucleotide Array Sequence Analysis - statistics & numerical data
• ISSN: 1064-3745
• DOI: 10.1007/978-1-59745-522-0_8

Differential methylation hybridization (DMH) is a high-throughput DNA methylation screening tool that utilizes methylation-sensitive restriction enzymes to profile methylated fragments by hybridizing them to a CpG island microarray. This array contains probes spanning all the 27,800 islands annotated in the UCSC Genome Browser. Herein we describe a revised DMH protocol with clearly identified quality control points. In this manner, samples that are unlikely to provide good readouts for differential methylation profiles between the test and the control samples will be identified and repeated with appropriate modifications. In addition to the step-by-step laboratory DMH protocol, we also provide a detailed description regarding DMH data analysis. The suggested microarray platform contains 244,000 probes and it can be a daunting barrier for researchers with no prior experience in analyzing DNA methylation data. We have created a data analysis pipeline available in a user friendly, publicly available interface, the Broad Institute's GenePattern software, which can be accessed at http://bisr.osumc.edu :8080/gp. This permits scientists to use our existing data analysis modules on their own data. As we continue to update our analysis algorithm and approaches to integrate high-throughput methylation data with other large-scale data types, we will make these new computation protocols available through the GenePattern platform.