The deorder 5(6)-ROX methylated probes using Fisher exact test as implemented by the R phyper function. Since we tested for both over and under-representation in the 7 different island classes we used a conservative p-value threshold. Affymetrix expression values used in Figure 3, and Figure S6 and Figure S7 in File S1were obtained using the Bioconductor implementation of the Mas5 method. Plots of expression vs. methylation used the kde2d function of the MASS package Genes that were up or down-regulated as a result of AZA or DAC treatment were identified by a profile similarity search implemented in eXintegrator, combined with a minimal two-fold change in mean expression values between treatment and mock samples. Changes in CGI methylation in tumours has been hypothesized to constitute a distinct phenotype and demethylation of tumour suppressor CGIs was reported in MDS patients after treatments with AZA or DAC. In order to analyse DNA methylation throughout the genome, we employed a method based on the ability of the McrBC enzyme to cut DNA at methylated CpG positions separated by 40 to 3000 bases. Digestion of fragmented DNA with this enzyme followed by comparative hybridisation to micro-arrays with non-digested DNA has been shown to be an effective means of detecting differentially methylated regions. Following McrBC digestion labelled probes were hybridized on microarrays containing probes tiled across all CGIs. To correlate DNA methylation with microarray data, we used bisulfite sequencing to quantify DNA methylation at a selection of CGI probes lying in the HOXA cluster. A fifty percent level of methylation corresponded approximately to a log2 ratio of 1. To reduce the influence of noise from badly performing probes we first selected a set of 52915 probes that showed low within replicate variance. These probes were then classified into methylation high, low, and medium classes. Most CGIs fell into the methylation low category, and only a small fraction of regions were highly methylated. We mapped CpG islands according to their overlap or proximity to gene CPDA features. The percentage of highly methylated probes around promoters was significantly under-represented whereas probes associated with the gene bodies were over-represented. Next, we aimed to identify probes
