Error minimization algorithm for comparative quantitative PCR analysis: Q-Anal.
Current methods for comparative quantitative polymerase chain reaction (qPCR) analysis, the threshold and extrapolation methods, either make assumptions about PCR efficiency that require an arbitrary threshold selection process or extrapolate to estimate relative levels of messenger RNA (mRNA) transcripts. Here we describe an algorithm, Q-Anal, that blends elements from current methods to by-pass assumptions regarding PCR efficiency and improve the threshold selection process to minimize error in comparative qPCR analysis. This algorithm uses iterative linear regression to identify the exponential phase for both target and reference amplicons and then selects, by minimizing linear regression error, a fluorescence threshold where efficiencies for both amplicons have been defined. From this defined fluorescence threshold, cycle time (Ct) and the error for both amplicons are calculated and used to determine the expression ratio. Ratios in complementary DNA (cDNA) dilution assays from qPCR data were analyzed by the Q-Anal method and compared with the threshold method and an extrapolation method. Dilution ratios determined by the Q-Anal and threshold methods were 86 to 118% of the expected cDNA ratios, but relative errors for the Q-Anal method were 4 to 10% in comparison with 4 to 34% for the threshold method. In contrast, ratios determined by an extrapolation method were 32 to 242% of the expected cDNA ratios, with relative errors of 67 to 193%. Q-Anal will be a valuable and quick method for minimizing error in comparative qPCR analysis.
OConnor
William
W
Department of Chemistry and Biochemistry, San Francisco State University, San Francisco, CA 94132, USA.
Runquist
Elizabeth A
EA
D000465:Algorithms; D018076:DNA, Complementary / Q000032:analysis; D015870:Gene Expression; D007201:Indicator Dilution Techniques; D016133:Polymerase Chain Reaction / Q000379:methods*; D015203:Reproducibility of Results; D012107:Research Design
D018076:DNA, Complementary