Correction of PCR-bias in quantitative DNA methylation studies by means of cubic polynomial regression.
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Preferential amplification of unmethylated alleles-known as PCR-bias-may significantly affect the accuracy of quantification.This study presents an effective method of correcting biased methylation data.The process can be applied irrespective of the locus interrogated and the number of sites analysed, avoiding an optimization of the amplification conditions for each individual locus.
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PubMed Central - PubMed
Affiliation: Functional Genome Analysis, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. e.moskalev@dkfz-heidelberg.de
ABSTRACT
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DNA methylation profiling has become an important aspect of biomedical molecular analysis. Polymerase chain reaction (PCR) amplification of bisulphite-treated DNA is a processing step that is common to many currently used methods of quantitative methylation analysis. Preferential amplification of unmethylated alleles-known as PCR-bias-may significantly affect the accuracy of quantification. To date, no universal experimental approach has been reported to overcome the problem. This study presents an effective method of correcting biased methylation data. The procedure includes a calibration performed in parallel to the analysis of the samples under investigation. DNA samples with defined degrees of methylation are analysed. The observed deviation of the experimental results from the expected values is used for calculating a regression curve. The equation of the best-fitting curve is then used for correction of the data obtained from the samples of interest. The process can be applied irrespective of the locus interrogated and the number of sites analysed, avoiding an optimization of the amplification conditions for each individual locus. Related in: MedlinePlus |
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Mentions: Applying the criterion of minimizing the sum of squared errors (Table 2), cubic polynomial fit curves were found to improve substantially the correction results at 25% methylation (Figure 2). The following equation was used for curve fitting:(4)where a, c, d and e are arbitrary parameters. The fitting was performed followed by solving the cubic equations for the unknown x with Cardano’s method described elsewhere (26). Typical reductions of the relative errors were from 30% (hyperbolic) to 13% (cubic polynomial) for SFRP2 (Figure 2) and 23% (hyperbolic) to 7% (cubic polynomial) for DKK1, for example. Also, cubic polynomial fit curves could be applied generally, achieving at least the effectiveness of correction obtained with the hyperbolic fit curves (Table 2, Figure 3). The only exception was the gene SFRP1; due to the enormous bias towards the methylated allele, better correction effectiveness was achieved by using hyperbolic regression, which accommodated more readily the defined 0% and 100% values.Figure 2. |
View Article: PubMed Central - PubMed
Affiliation: Functional Genome Analysis, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. e.moskalev@dkfz-heidelberg.de