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

Genome Biology e2VHt0oea0lul7.emtmheao8n,sdIssue 2, Article R19 Me qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data Jan Hellemans 0 Geert Mortier 0 Anne De Paepe 0 Frank Speleman 0 Jo Vandesompele 0 0 Address: Center for Medical Genetics, Ghent University Hospital , De Pintelaan, B-9000 Ghent , Belgium 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. - Background Since its introduction more than 10 years ago [1], quantitative PCR (qPCR) has become the standard method for quantification of nucleic acid sequences. The ease of use and high sensitivity, specificity and accuracy has resulted in a rapidly expanding number of applications with increasing throughput of samples to be analyzed. The software programs provided along with the various qPCR instruments allow for straightforward extraction of quantification cycle values from the recorded fluorescence measurements, and at best, interpolation of unknown quantities using a standard curve of serially diluted known quantities. However, these programs usually do not provide an adequate solution for the processing of these raw data (coming from one or multiple runs) into meaningful results, such as normalized and calibrated relative quantities. Furthermore, the currently available tools all have one or more of the following intrinsic limitations: dedicated for one instrument, cumbersome data import, a limited number of samples and genes can be processed, forced number of replicates, normalization using only one reference gene, lack of data quality controls (for example, replicate variability, negative controls, reference gene expression stability), inability to calibrate multiple runs, limited result visualization options, lack of experimental archive, and closed software architecture. To address the shortcomings of the available software tools and quantification strategies, we modified the classic deltadelta-Ct method to take multiple reference genes and gene specific amplification efficiencies into account, as well as the errors on all measured parameters along the entire calculation track. On top of that, we developed an inter-run calibration algorithm to correct for (often underestimated) run-torun differences. Our advanced models and algorithms are implemented in qBase, a flexible and open source program for qPCR data management and analysis. Four basic principles were followed during development of the program: the use of correct models and formulas for quantification and error propagation, inclusion of data quality control where required, automation of the workflow as much as possible while retaining flexibility, and user friendliness of operation. Our quantification framework and software fit exactly in current thinking that places emphasis on getting every step of a realtime PCR assay right (such as RNA quality assessment, appropriate reverse transcription, selection of a proper normalization strategy, and so on [2]), especially if small differences between samples need to be reliably demonstrated. In this entire workflow, data analysis is an important last step. Results and discussion Determination of the error on estimated amplification efficiencies qBase employs a proven, advanced and universally applicable relative quantification model. An important underlying assumption is that PCR efficiency is assay dependent and sample independent. While this may not be true in every experimental situation, there is currently no consensus on how sample specific PCR efficiencies should be calculated and used for robust quantification. Most evaluation studies attribute a lack of precision to these sample specific efficiency estimation methods. Hence, the gold standard is still the use of a PCR efficiency estimated by a serial dilution series (preferably of pooled cDNA samples, to mimic as much as possible the actual samples to be measured), at least if one aims at accurate and precise quantification. Sample specific PCR efficiency estimation has its usefulness, but currently only for outlier detection [3-5]. Calculation of relative quantities from quantification cycle values requires knowledge of the amplification efficiency of the PCR. As stated above, amplicon specific amplification efficiencies are preferably determined using linear regression (formulas 1 and 5 in Materials and methods) of a serial dilution series with known quantities (either relative or absolute). However, the error on the estimated amplification efficiency is almost never determined, nor taken into account. This error can be calculated using linear regression as well (formulas 2 to 4 and 6), and should subsequently be propagated during conversion of the quantification cycle values to the relative quantities. The formula for the error on the slope provides the mathematical basis to learn how more accurate amplification efficiency estimates can be achieved, that is, by expanding the range of the dilution and including more measurement points. Calculation of normalized relative quantities and error minimization Methods for the conversion of quantification cycle values (Cq; see Materials and methods for terminology) into normalized relative quantities (NRQs) were first reported in 2001. The simplest model described by Livak and Schmittgen [6] assumes 100% PCR efficiency (reflected by a value of 2 for the base E of the exponential function) and uses a single reference gene for normalization: Pfaffl [7] modified the above model by adjusting for differences in PCR efficiency between the gene of interest (goi) and a reference gene (ref): NRQ = This model constituted an improvement over the classic delta-delta-Ct method, but cannot deal with multiple (f) reference genes, which is required for reliable measurements of subtle expression differences [8]. Therefore, we further extended this model to take into account multiple stably expressed reference genes for improved normalization. Although not yet published, this advanced and generalized model of relative quantification has been applied previously in our nucleic acid quantification studies [8-12]. NRQ = The calculation of relative quantities, normalization and corresponding error propagation is detailed in formulas 7-16. The basic principle of the delta-Cq quantification model is that a difference (delta) in quantification cycle value between two samples (often a true unknown and calibrator or reference sample) is transformed into relative quantities using the exponential function (...truncated)