Mol Cell Biochem. 2026 Jul 1. doi: 10.1007/s11010-026-05621-y. Online ahead of print.
ABSTRACT
Quantitative polymerase chain reaction (qPCR) is an indispensable tool in clinical biochemistry laboratories for gene expression and copy number variation (CNV) analyses. However, the interpretation of qPCR data including normalization, rigorous statistical testing, and professional visualization typically requires advanced bioinformatics expertise. This study aimed to develop a user-friendly, web-based platform that integrates robust statistical frameworks and quality control modules for streamlined and standardized qPCR data evaluation. GeneQuantify is freely accessible at https://GeneQuantify.streamlit.app/ and its source code is openly available at https://github.com/burhanettiny/GeneQuantify (GPL-3.0 license). GeneQuantify, a web-based application developed using Python and Streamlit, allows for the input of target and reference gene Cq values via manual entry or direct import from spreadsheet software or standardized RDML/RDES file formats. The platform automatically calculates ΔCq, ΔΔCq, and relative expression levels using the 2^(-ΔΔCq) method. Integrated features include multi-reference gene normalization (geNorm), automated outlier detection (Grubbs’ test or Interquartile Range), and amplification efficiency correction (Pfaffl model). ΔCt values are subjected to normality (Shapiro-Wilk) and variance homogeneity (Levene’s) testing to ensure statistical validity. The platform features an automated statistical decision pipeline (Shapiro-Wilk → Levene → t/Welch/Mann-Whitney/ANOVA/Kruskal-Wallis) with Bonferroni and Benjamini-Hochberg FDR corrections. A six-language interface (Turkish, English, German, French, Spanish, and Arabic) ensures international accessibility. Platform accuracy was validated against manual Excel-based calculations across seven predefined test scenarios, yielding consistent results in all cases. GeneQuantify provides a highly accessible, integrated qPCR analysis environment that consolidates automated calculations, quality control, statistical decision-making, and visualization. By aligning with MIQE guidelines and offering RQ-based automated statistical selection, the platform enhances reproducibility, transparency, and workflow efficiency in molecular research, clinical biochemistry, and educational settings.
PMID:42384344 | DOI:10.1007/s11010-026-05621-y
