Correction of measurement biases in DNA methylation studies by means of next generation sequencing and hybridisation on oligonucleotide microarrays

Quantitative analysis of DNA methylation patterns is an intrinsic part of epigenetic cancer research. Besides, detection of tumour specific epigenetic aberrations holds promise for cancer diagnostics. PCR amplification of loci of interest from bisulfite treated DNA is a common sample preparation step. Given the fact amplicons and not the genomic DNA is used for quantification of the methylation percentages, the PCR amplification step is limiting factor for the accuracy of the entire analysis. Preferential amplification of either methylated or unmethylated alleles may hamper the correct interpretation of results. Additional biases may be introduced by the quantification technologies on their own, for example the specificity of oligonucleotide probes of a microarray. Aim. The aim of present work was to evaluate the applicability of the cubic polynomial regression for correction of measurement biases in quantitative DNA methylation data that were obtained by the next generation sequencing and hybridisation analysis on the oligonucleotide microarrays. Materials and Methods. Next generation bisulfite 454 sequencing and hybridisation on the in situ synthesised microarrays (Febit Biotech) was employed to quantify DNA methylation in vicinity of the transcriptional start sites. Cubic polynomial regression was used to analyse the calibration data. Results. Candidate genes were selected that exhibit aberrant DNA methylation patterns in cancer. Specifically, these included genes coding for the subunits of succinate dehydrogenase ( SDH ) as well as tumour suppressor genes CDKN2B , DAPK1 and TP53 . The interrogated DNA loci were amplified from control DNA samples of defined methylation percentages. Substantial deviations of apparent methylation percentages from theoretically expected values were detected by the sequencing of SDHB and SDHD amplicons. Likewise, the methylation indices of the CDKN2B and DAPK1 CpG sites that were analysed were significantly biased in the microarray data. Correction of biased data was performed by using the equations of respective regression curves resulting in almost complete removal of the artefacts. Conclusions. Successful application of cubic polynomial regression was achieved for correcting measurement biases in two different types of quantitative DNA methylation data - namely next generation bisulfite sequencing and hybridisation on the oligonucleotide microarrays - demonstrating the universal applicability of this correction process.

метилирование ДНК, количественный анализ, ПЦР-отклонение, кубическая регрессия, высокопроизводительное бисульфитное секвенирование, олигонуклеотидные ДНК-чипы, DNA methylation, quantitative analysis, PCR-bias, cubic polynomial regression, next-generation sequencing, oligonucleotide microarrays

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