Copy number variation-based read-count normalization of MBD-Seq of glioblastoma multiforme (GBM) tumour samples data using Illumina Infinium 450k platform.

<p>The use of methylated DNA capturing followed by massive parallel sequencing has become a robust method for the study of genome-wide methylation patterns. However, it has recently been demonstrated that the accurate estimation of methylation levels is dependent on incorporating CNV status of the sampled genomic regions (Robinson et al, 2012).</p> <p>We used Illumina multiplex paired-end (PE) sequencing of MBD-captured DNA isolated from GBM samples. The mean number of PE reads per library was 5.3 million [19.3-2 million]. Alignment of quality-controlled (min. Q20) and de-duplicated reads to the human reference genome hg19 was performed using bowtie2, resulting in a mean number of mapped PE reads of 3.8 million [14-1 million]. Our preliminary results indicate differences in the methylation of CpG islands located in promoter regions when comparing normal versus tumour samples. We also performed DNA methylation estimation using the Infinium 450k platform for the same set of samples. Illumina's Infinium HumanMethylation450 technology is based on the OmniBeadChip genotyping technology which has been used to accurately estimate CNVs. We therefore hypothesized that the 450k methylation data (signal intensities) could be used for the estimation of the CNV status. One pre-requisite for this approach is the availability of a canonical set of controls generated from normal tissue. Due to the limited sample size of our data set, we plan to incorporate the 450k methylation and CNV data available from TCGA to:</p> <p>1. create the canonical set of controls of 450k data from normal tissues of TCGA samples</p> <p>2. perform CNV estimation of TCGA samples based on 450k methylation data</p> <p>3. verify the accuracy of the 450k-based approach against TCGA CNV data</p> <p>The use of TCGA data in this methodology will allow our laboratory to evaluate the robustness of this approach before applying it to our own datasets.