qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data

Abstract

Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.

Figure 1

Effect of reference quantification cycle value on increase in error. Relative quantities were calculated for a simulated experiment with a five point four-fold dilution series using, respectively, the lowest Cq (squares), the average Cq (circles) or the highest Cq (triangles) as the reference quantification cycle value. Cq and quantity values are shown at the top left. The increase in the error on relative quantities for the different samples is shown at the top right, with the average increase depicted on the lower left graph.

Figure 2

Experimental setup. Experimental setup used to evaluate the effects of inter-run calibration. On the right side, a sample maximization approach is used to analyze 6 genes for 11 samples in 1.5 run. With gene maximization (left side), IRCs (S1, S2, S3) are required to allow comparison of S5-S7 (run 1) to S8-S11 (run 2 or 3), thus requiring two full runs. The IRCs in run 2 are measured on the same cDNA dilution whereas the IRCs in run 3 are measured on newly prepared cDNA from the same RNA.

Figure 3

Experimental data comparing sample and gene maximization. The sample maximization approach (run 5) is compared to the gene maximization approach (runs 1 and 2 or 1 and 3). The difference between the IRCs is 0.77 for the Cq values, 72% for the NRQ values, and eliminated after inter-run calibration. Grey and white within the same display item indicates that data comes from different runs.

Figure 4

qBase. (a) qBase start up screen; (b) import wizard allowing selection of the format of the input file; (c) standard curve with a five point four-fold dilution series used to calculate the amplification efficiency; (d) qBase Analyzer main window with the workflow on the right and sample and gene list on the left - special sample types and reference genes are highlighted; (e) single gene histogram; (f) multi-gene histogram.

Figure 5

qBase calculation workflow.