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Model based analysis of real-time PCR data from DNA binding dye protocols.


ABSTRACT:

Background

Reverse transcription followed by real-time PCR is widely used for quantification of specific mRNA, and with the use of double-stranded DNA binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time PCR, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same.

Results

We present here a new mathematical model based on the classic exponential description of the PCR, but modeling amplification efficiency as a sigmoidal function of the product yield. The model was validated with experimental results and used for the development of a new method for real-time PCR data analysis. This model based method for real-time PCR data analysis showed the best accuracy and precision compared with previous methods when used for quantification of in-silico generated and experimental real-time PCR results. Moreover, the method is suitable for the analyses of samples with similar or dissimilar amplification efficiency.

Conclusion

The presented method showed the best accuracy and precision. Moreover, it does not depend on calibration curves, making it ideal for fully automated high-throughput applications.

SUBMITTER: Alvarez MJ 

PROVIDER: S-EPMC1838433 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Model based analysis of real-time PCR data from DNA binding dye protocols.

Alvarez Mariano J MJ   Vila-Ortiz Guillermo J GJ   Salibe Mariano C MC   Podhajcer Osvaldo L OL   Pitossi Fernando J FJ  

BMC bioinformatics 20070309


<h4>Background</h4>Reverse transcription followed by real-time PCR is widely used for quantification of specific mRNA, and with the use of double-stranded DNA binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time PCR, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same.<h4>Results</h4>We present here a new mathematica  ...[more]

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