FCI: an R-based algorithm for evaluating uncertainty of absolute real-time PCR quantification

In order to describe the features of FCI four situations were selected from real data collected during an international external quality assessment program for quantitative assays based on real-time PCR. The code generates a diagnostic figure suitable for assessing the quality of the quantification process.We have provided a freeware programme using this algorithm specifically designed to increase the information content of the real-time PCR assay.Real-time PCR is widely used for the quantification of nucleic acids in a wide range of clinical and research applications including the measurement of gene dosage, detection of residual disease in haematological malignancies and detection of bacterial and viral infection.Real-time PCR typically employs fluorescent probes which generate a signal that accumulates during PCR cycling in a manner proportional to the concentration of amplification products. Absolute quantification of a nucleic acid target can be achieved using a standard curve, which is generated by amplifying known amounts of the target DNA. The standard curve is typically generated using a series of 10-fold dilutions of a control template. For each dilution, replicated determinations of the cycle threshold (ct) are performed and a straight line is fitted to the data by plotting the ct averages as a function of the logarithm of the starting concentration of the standards. By applying a technique known as "inverse regression," the straight line is used as a "calibrator" to estimate the unknown starting DNA concentration in the sample under examination.As in any titration, evaluating the uncertainty in the estimated concentration of the unknown sample is critical for interpreting the data and optimizing experimental procedures. In addition to the point estimate it is important to calculate the confidence interval of the "true" value of the unknown concentration.Commercial software specifically designed for generating standard curves and estimating nucleic acid c