MEDME: An experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment

Abstract

DNA methylation is an important component of epigenetic modifications that influences the transcriptional machinery and is aberrant in many human diseases. Several methods have been developed to map DNA methylation for either limited regions or genome-wide. In particular, antibodies specific for methylated CpG have been successfully applied in genome-wide studies. However, despite the relevance of the obtained results, the interpretation of antibody enrichment is not trivial. Of greatest importance, the coupling of antibody-enriched methylated fragments with microarrays generates DNA methylation estimates that are not linearly related to the true methylation level. Here, we present an experimental and analytical methodology, MEDME (modeling experimental data with MeDIP enrichment), to obtain enhanced estimates that better describe the true values of DNA methylation level throughout the genome. We propose an experimental scenario for evaluating the true relationship in a high-throughput setting and a model-based analysis to predict the absolute and relative DNA methylation levels. We successfully applied this model to evaluate DNA methylation status of normal human melanocytes compared to a melanoma cell strain. Despite the low resolution typical of methods based on immunoprecipitation, we show that model-derived estimates of DNA methylation provide relatively high correlation with measured absolute and relative levels, as validated by bisulfite genomic DNA sequencing. Importantly, the model-derived DNA methylation estimates simplify the interpretation of the results both at single-loci and at chromosome-wide levels.

Next Generation Sequencing - Advances, Applications and Challenges

“…A number of computational tools have been developed for the analysis of MeDIP data (Table 1), including Batman [33], MEDME [37], MEDIPS [38], MeQA [39] and MeDUSA [40]. The method to use depends very much on the questions you want to ask of the data, and as a result the type of analysis performed can be described as analyzing absolute methylation or, alternatively, relative methylation.…”

Evaluation of affinity-based genome-wide DNA methylation data: Effects of CpG density, amplification bias, and copy number variation

“…We use the definition of local CpG density given by Pelizzola et al (2008), with a window of 600 bp (Pelizzola et al 2008) since we hybridize genomic DNA fragments with an average length of 600 bases, and individual probes are measuring signal from adjacent genomic regions and are thus affected by the number of CpG sites in this region. Briefly, the local CpG density is a weighted count of CpG sites in the genome upstream and downstream 600 bases from a given point of interest (e.g., microarray probe location).…”

Assessment of methylation level prediction accuracy in methyl-DNA immunoprecipitation and sodium bisulfite based microarray platforms

“…Several strategies have been developed aimed at correcting these and other biases, in order to increase its prediction capabilities. 36,37,39,40 However, none of the three corrections we employed improved the accuracy of the prediction of intermediate methylation data. The Batman correction model was developed from data obtained from an array platform that targeted promoter regions which were both CpG poor and CpG rich.…”