In molecular biology, the real-time quantitative polymerase chain reaction (RT-qPCR) has become the most powerful method for the detection and quantification of nucleic acid sequences including gene expressions. The technique is widely used for example for the validation of genes differentially expressed in transcriptomic experiments, such as microarrays or RNAseq. Several methods specific for each qPCR machine manufacturers’ have been developed to extract quantification cycle values (Cq) from recorded fluorescence measurements. The process of these raw data is however not always adequate for the biologist because they lack for example normalization steps and are often dedicated to a single instrument.
We decided to implement a web tool called SATqPCR and based on the RqPCRAnalysis R-package, an algorithm we developed in R language. The R-package was developed in our lab by Trang Tran and Frederique Hilliou and was presented at the International Conference on Bioinformatics Models, Methods and Algorithms in 2013 (see references). The purpose of this tool is to provide a comprehensive, simple and easy to use tool for real-time quantitative PCR data analysis. More importantly, functions are provided for biologists who have little statistical and no R programming background. This SATqPCR improves the methods developed by Genorm and qBASE programs. We have developed this web application for biologist using the MIQE recommendation for the analysis of their qPCR data (Minimum Information for Publication of Quantitative Real-Time PCR Experiments, Bustin et al. 2010, Bustin et al. 2002)Our application provides:
- The identification of the most stable reference genes (REF)--housekeeping genes--across biological replicates and technical replicates
- The calculation of normalization factor based on the identified REF
- The calculation of normalized expression for each gene of interest (GOI)
- The rescaling of the normalized expression across biological replicates
- The comparison of expression level of GOI between samples across biological replicates using appropriate statistical test