Analysis of qRT-PCR Data to Identify the Most Stable Reference Gene Using gQuant

The accurate quantification of nucleic acid-based biomarkers, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs), is essential for disease diagnostics and risk assessment across the biological spectrum. Quantitative reverse transcription PCR (qRT-PCR) is the gold standard assay for the quantitative measurement of RNA expression levels, but its reliability depends on selecting stable reference targets for normalization. Yet, the lack of consensus on a universally accepted reference gene for a given sample type or species, despite being necessary for accurate quantification, presents a challenge to the broad application of such biomarkers. Various tools are currently being used to identify a stably expressed gene by using qRT-PCR data of a few potential normalizer genes. However, existing tools for normalizer gene selection are fraught with both statistical limitations and inadequate graphical user interfaces for data visualization. gQuant, the tool presented here, essentially overcomes these limitations. The tool is structured in two key components: the preprocessing component and the data analysis component. The preprocessing addresses missing values in the given dataset by the imputation strategies. After data preprocessing, normalizer genes are ranked using democratic strategies that integrate predictions from multiple statistical methods. The effectiveness of gQuant was validated through data available online as well as in-house data derived from urinary exosomal miRNA expression datasets. Comparative analysis against existing tools demonstrated that gQuant delivers more stable and consistent rankings of normalizer genes. With its promising performance, gQuant enhances the precision and reproducibility in the identification of normalizer genes across diverse research scenarios, addressing key limitations of RNA biomarker-based translational research. Key features • Accurate reference gene selection: gQuant identifies the most stable gene in qRT-PCR datasets using a multi-metric approach including SD, GM, CV, and KDE. • Robust missing data handling: Implements imputation and removal strategies to ensure data integrity and accurate normalizer selection. • Bias-free ranking algorithm: Utilizes a voting-based classifier to provide fair and consistent ranking, overcoming limitations of weighted approaches. • Comprehensive visualization: Offers boxplots and KDE plots for analyzing gene expression variability, aiding in data interpretation.