Development of a robust and generalizable algorithm "gQuant" for accurate normalizer gene selection in qRT-PCR analysis.

The emergent role of nucleic acid-based biomarkers-microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [ https://github.com/ABHAYHBB/gQuant-Tool ]."