A Bayesian method for calculating real-time quantitative PCR calibration curves using absolute plasmid DNA standards

• Published in: BMC bioinformatics Vol. 9; no. 1; p. 120
• Main Authors: Sivaganesan, Mano, Seifring, Shawn, Varma, Manju, Haugland, Richard A, Shanks, Orin C
• Format: Journal Article
• Language: English
• Published: London BioMed Central 25.02.2008; BioMed Central Ltd; BMC
• Subjects: Algorithms; Base Sequence; Bayes Theorem; Bioinformatics; Biomedical and Life Sciences; Calibration; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Identification and classification; Life Sciences; Methodology; Methodology Article; Methods; Microarrays; Molecular Sequence Data; Pattern Recognition, Automated - methods; Plasmids; Plasmids - genetics; Plasmids - standards; Polymerase chain reaction; Reverse Transcriptase Polymerase Chain Reaction - instrumentation; Reverse Transcriptase Polymerase Chain Reaction - standards; Sequence Analysis, DNA - methods; Sequence Analysis, DNA - standards; United States; United States; Monte Carlo Markov Chain; Posterior Distribution; Bayesian Credible Interval; Monte Carlo Markov Chain Method; Monte Carlo Markov Chain Approach
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-9-120

Background In real-time quantitative PCR studies using absolute plasmid DNA standards, a calibration curve is developed to estimate an unknown DNA concentration. However, potential differences in the amplification performance of plasmid DNA compared to genomic DNA standards are often ignored in calibration calculations and in some cases impossible to characterize. A flexible statistical method that can account for uncertainty between plasmid and genomic DNA targets, replicate testing, and experiment-to-experiment variability is needed to estimate calibration curve parameters such as intercept and slope. Here we report the use of a Bayesian approach to generate calibration curves for the enumeration of target DNA from genomic DNA samples using absolute plasmid DNA standards. Results Instead of the two traditional methods (classical and inverse), a Monte Carlo Markov Chain (MCMC) estimation was used to generate single, master, and modified calibration curves. The mean and the percentiles of the posterior distribution were used as point and interval estimates of unknown parameters such as intercepts, slopes and DNA concentrations. The software WinBUGS was used to perform all simulations and to generate the posterior distributions of all the unknown parameters of interest. Conclusion The Bayesian approach defined in this study allowed for the estimation of DNA concentrations from environmental samples using absolute standard curves generated by real-time qPCR. The approach accounted for uncertainty from multiple sources such as experiment-to-experiment variation, variability between replicate measurements, as well as uncertainty introduced when employing calibration curves generated from absolute plasmid DNA standards.