The Evolution of Gene Expression Analysis: Introducing the gQuant Tool
In the realm of molecular biology, the spotlight has increasingly focused on nucleic acid-based biomarkers such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs), which play pivotal roles in disease diagnostics and risk assessment. Quantitative reverse transcription PCR (qRT-PCR) stands as the cornerstone technique for quantifying these vital markers. However, a significant challenge persists due to the non-coding RNAs’ relatively recent emergence as biomarkers and the consequent lack of consensus on a universally accepted normalizer gene, which is crucial for accurate quantification.
The current landscape of tools for normalizer selection, such as RefFinder, which relies on Ct values from experiments to gauge gene stability, is marred by statistical shortcomings and often fails to address the unique challenges presented, including the handling of null values and complex biological variability. This situation underscores a pressing need for a more balanced and adaptable tool tailored specifically for the nuances of qRT-PCR datasets.
In response to these challenges, we have developed the ‘gQuant’ tool, leveraging the power of voting classifiers that amalgamate predictions from multiple statistical methodologies to identify the most stable normalizer genes. This tool exhibited superior performance in validations using various datasets, including in-house derived data from urinary exosomal miRNAs, setting a new standard in normalizer gene identification. The results show that ‘gQuant’ consistently generates rankings characterized by lower standard deviations, reduced covariance, and improved kernel density estimation values when compared with existing tools.
The development of ‘gQuant’ signifies a pivotal shift towards enhancing the precision of gene expression quantification across diverse research scenarios. ‘gQuant’ is made publicly available, ensuring that the broader research community can benefit from this advanced tool for more reliable and accurate gene expression studies. Interested researchers can access it at https://github.com/ABHAYHBB/gQuant-Tool.
MicroRNAs, due to their stability and abundance, have become promising molecules in diagnostics and therapeutics, especially in the domain of cancer research. Urinary extracellular vesicles (uEVs) have emerged as an important source for miRNA biomarkers, especially for non-invasive detection and monitoring of urological cancers. However, the lack of a robust and standardized normalizer has led to varied normalization strategies, which in turn, has raised concerns regarding the comparability and reliability of study results.
In addressing this gap, the development and validation of the ‘gQuant’ tool marks a significant leap forward. By introducing a more robust and reliable method for normalizer gene identification, ‘gQuant’ promises to streamline the analysis of qRT-PCR expression data, thereby facilitating more accurate and reproducible results in the field of molecular diagnosis and beyond.
As researchers and scholars continue to navigate the complex landscape of gene expression analysis, tools like ‘gQuant’ serve as beacons of innovation, guiding the way towards more standardized, reliable, and generalizable research methodologies. It is through such advancements that the scientific community can hope to achieve a higher degree of precision and consistency in its ongoing quest to understand and combat various diseases at the molecular level.
