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 advanced models and algorithms are implemented in qBase, a flexible and open source program for qPCR data management and analysis. On top of that, we developed an inter-run calibration algorithm to correct for (often underestimated) run-to-run differences. To address the shortcomings of the available software tools and quantification strategies, we modified the classic delta-delta-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. 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. 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. 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. 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.
Since its introduction more than 10 years ago, quantitative PCR (qPCR) has become the standard method for quantification of nucleic acid sequences.
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