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Real Time RT-PCR

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Updated: Jun 17, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

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

Abhay Kumar Pathak¹, Sukhad Kural², Shweta Singh³

¹DST-CIMS, Institute of Science, Banaras Hindu University, Varanasi, India.

Scientific Reports

|August 13, 2024

Summary

A new tool, gQuant, improves the accuracy of gene expression quantification by selecting reliable normalizer genes for qRT-PCR analysis. This addresses challenges in biomarker discovery using microRNAs, long non-coding RNAs, and messenger RNAs.

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Area of Science:

Biomarker Discovery
Molecular Diagnostics
Bioinformatics

Background:

Nucleic acid biomarkers (miRNAs, lncRNAs, mRNAs) are crucial for disease diagnostics.
Quantitative reverse transcription PCR (qRT-PCR) is the standard for biomarker measurement.
Accurate qRT-PCR requires reliable normalizer genes, which are currently lacking for non-coding RNAs.

Purpose of the Study:

To develop a robust tool for normalizer gene selection in qRT-PCR.
To overcome statistical limitations and poor visualization in existing normalizer selection tools.
To enhance the precision of gene expression quantification for biomarker analysis.

Main Methods:

Developed 'gQuant', a novel tool utilizing voting classifiers to combine multiple statistical methods.
Validated gQuant using diverse urinary exosomal miRNA datasets.
Compared gQuant's performance against existing normalizer selection tools.

Main Results:

gQuant demonstrated superior normalizer gene ranking compared to existing tools.
Rankings from gQuant showed lower standard deviation and covariance.
Enhanced kernel density estimation values indicated improved data distribution analysis.
gQuant avoids the skewed rankings sometimes produced by other methods.

Conclusions:

gQuant significantly improves normalizer gene identification for qRT-PCR.
The tool enhances the precision of gene expression quantification across various research settings.
gQuant offers a balanced solution for analyzing qRT-PCR data, particularly for non-coding RNA biomarkers.
The gQuant tool is publicly available for use.