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Correction of PCR-bias in quantitative DNA methylation studies by means of cubic polynomial regression.

Moskalev EA, Zavgorodnij MG, Majorova SP, Vorobjev IA, Jandaghi P, Bure IV, Hoheisel JD - Nucleic Acids Res. (2011)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Functional Genome Analysis, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. e.moskalev@dkfz-heidelberg.de

ABSTRACT
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.

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Influence of the number of calibration samples on the accuracy of bias correction. An analysis which was corrected on the basis of only five (0, 25, 50, 75 and 100% methylation), four (0, 25, 75 and 100%) or three (0, 50 and 100%) DNA samples resulted in essentially similarly correct data. The blue bars represent the raw data; the bars of different shades of red show the corrected values. In addition to each methylation percentage value, also the relative error is indicated. The actual methylation percentages are listed at the bottom.
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Figure 4: Influence of the number of calibration samples on the accuracy of bias correction. An analysis which was corrected on the basis of only five (0, 25, 50, 75 and 100% methylation), four (0, 25, 75 and 100%) or three (0, 50 and 100%) DNA samples resulted in essentially similarly correct data. The blue bars represent the raw data; the bars of different shades of red show the corrected values. In addition to each methylation percentage value, also the relative error is indicated. The actual methylation percentages are listed at the bottom.

Mentions: The PCR-bias correction described so far was based on nine calibration samples. For simplifying the correction process, we examined if the number of controls could be reduced while still providing consistent correction power. Using the fit curves based on only five (0%, 25%, 50%, 75% and 100% methylation), four (0%, 25%, 75% and 100%) or three (0%, 50% and 100%) calibration samples, we investigated if we were able accurately to predict methylation degrees for the calibration samples of 37.5%, 62.5% and 87.5% methylation, using the genes with the largest bias (SFRP2, CDH1, DKK1 and DKK2). The corrected values were very similar by using down to three calibration samples (Figure 4). No major difference was observed in average relative errors compared to using nine calibration samples (Supplementary Figure S1).Figure 4.


Correction of PCR-bias in quantitative DNA methylation studies by means of cubic polynomial regression.

Moskalev EA, Zavgorodnij MG, Majorova SP, Vorobjev IA, Jandaghi P, Bure IV, Hoheisel JD - Nucleic Acids Res. (2011)

Influence of the number of calibration samples on the accuracy of bias correction. An analysis which was corrected on the basis of only five (0, 25, 50, 75 and 100% methylation), four (0, 25, 75 and 100%) or three (0, 50 and 100%) DNA samples resulted in essentially similarly correct data. The blue bars represent the raw data; the bars of different shades of red show the corrected values. In addition to each methylation percentage value, also the relative error is indicated. The actual methylation percentages are listed at the bottom.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3113592&req=5

Figure 4: Influence of the number of calibration samples on the accuracy of bias correction. An analysis which was corrected on the basis of only five (0, 25, 50, 75 and 100% methylation), four (0, 25, 75 and 100%) or three (0, 50 and 100%) DNA samples resulted in essentially similarly correct data. The blue bars represent the raw data; the bars of different shades of red show the corrected values. In addition to each methylation percentage value, also the relative error is indicated. The actual methylation percentages are listed at the bottom.
Mentions: The PCR-bias correction described so far was based on nine calibration samples. For simplifying the correction process, we examined if the number of controls could be reduced while still providing consistent correction power. Using the fit curves based on only five (0%, 25%, 50%, 75% and 100% methylation), four (0%, 25%, 75% and 100%) or three (0%, 50% and 100%) calibration samples, we investigated if we were able accurately to predict methylation degrees for the calibration samples of 37.5%, 62.5% and 87.5% methylation, using the genes with the largest bias (SFRP2, CDH1, DKK1 and DKK2). The corrected values were very similar by using down to three calibration samples (Figure 4). No major difference was observed in average relative errors compared to using nine calibration samples (Supplementary Figure S1).Figure 4.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Functional Genome Analysis, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. e.moskalev@dkfz-heidelberg.de

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus