qDATA - an R application implementing a practical framework for analyzing quantitative real-time PCR data

Gene expression assays that are based on quantitative real-time PCR (qRT-PCR) method are still very popular, therefore, we developed qDATA, an open-source R-based bioinformatics application that offers a quick and intuitive analysis of raw cycle threshold (Ct) values. The application relies on a straightforward data input consisting in Ct values and on other mandatory fields specifying the experimental and control groups. qDATA automatically performs descriptive statistics, normality and statistical testing on 2^–ΔCt (or ΔCt) and 2^–ΔΔCt terms calculated with Livak’s method. We also propose a qRT-PCR data analysis framework that depends on performing exhaustive ΔCt calculations within discrete biological replicates (BRs) and subsequently using the Livak formula for the complete sets of available data. These prerequisites arguably lead to an improved data analysis and statistical relevance. The efficiency of our computing approach was tested using input Ct values corresponding to immune related gene expression evaluated in experimental infection of <i>Drosophila melanogaster</i> and <i>Apis mellifera</i> workers. The presented results reveal that our working strategy is reliable and highlight the efficacy and performance of qDATA application. We developed qDATA, an open-source R based application designed for automated analysis of gene expression of data derived from qRT-PCR experiments. This tool provides a streamlined workflow with a modern and user-friendly graphical interface, enabling users to perform descriptive statistics, assess data normality and conduct statistical testing on 2^–ΔCt (or ΔCt) and 2^–ΔΔCt terms calculated with the Livak’s method. qDATA implements a strategy of calculating all possible differences between cycle threshold values within a biological replicate. In this article we showcase the functionality of qDATA and demonstrate its efficiency on two previously published qRT-PCR datasets, highlighting its practical application and effectiveness in gene expression studies. <b>Introduction</b>Currently, qRT-PCR is the method of choice for targeted gene expression experiments and the Livak formula is the most popular implementation for calculating fold change values. Currently, qRT-PCR is the method of choice for targeted gene expression experiments and the Livak formula is the most popular implementation for calculating fold change values. <b>Materials and methods</b>We developed qDATA, an open-source R based application designed for automated analysis of qRT-PCR data that is freely available to download from GitHub at https://github.com/DL-UB/dScaff. We developed qDATA, an open-source R based application designed for automated analysis of qRT-PCR data that is freely available to download from GitHub at https://github.com/DL-UB/dScaff. <b>Implementation</b>Supports the use of extended calculations for ΔCt values by implementing all possible differences between technical replicates within a biological replicate.Argues the efficiency of statistical testing and of calculating the fold change values when using this framework in comparison to the other implementation based on mean Ct values.Presents qDATA, an original bioinformatics tool that makes use of the proposed framework in an intuitive, fast and customizable GUI.Advocates for statistical testing on linear forms of ΔCt (2^-ΔCt) for consistent and reliable results.Provides a streamlined interface with intuitive data input and advanced parameter adjustments that enable comprehensive summary statistics, statistical testing, fold change analysis and various export features. Supports the use of extended calculations for ΔCt values by implementing all possible differences between technical replicates within a biological replicate. Argues the efficiency of statistical testing and of calculating the fold change values when using this framework in comparison to the other implementation based on mean Ct values. Presents qDATA, an original bioinformatics tool that makes use of the proposed framework in an intuitive, fast and customizable GUI. Advocates for statistical testing on linear forms of ΔCt (2^-ΔCt) for consistent and reliable results. Provides a streamlined interface with intuitive data input and advanced parameter adjustments that enable comprehensive summary statistics, statistical testing, fold change analysis and various export features. <b>Results and Discussion</b>Using actual qRT-PCR data, we put to test our application and made a detail comparison of the results obtained with two different analysis approaches (Case 1 and Case 2).We compare qDATA to existing tools of qRT-PCR data analysis.We intend to further develop qDATA in order to accommodate a wider range of research scenarios and to be more user friendly. Using actual qRT-PCR data, we put to test our application and made a detail comparison of the results obtained with two different analysis approaches (Case 1 and Case 2). We compare qDATA to existing tools of qRT-PCR data analysis. We intend to further develop qDATA in order to accommodate a wider range of research scenarios and to be more user friendly. <b>Conclusion:</b>qDATA offers fast, efficient and reliable automatic qRT-PCR data analysis, with no required programming experience. qDATA offers fast, efficient and reliable automatic qRT-PCR data analysis, with no required programming experience.