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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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Related Experiment Video

Updated: Apr 15, 2026

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

Published on: January 22, 2014

Reference gene validation for RT-qPCR, a note on different available software packages.

Ward De Spiegelaere¹, Jutta Dern-Wieloch², Roswitha Weigel²

¹Ghent University, Department of Internal Medicine, Ghent, Belgium.

|April 1, 2015

Summary

This summary is machine-generated.

Selecting stable reference genes for RT-qPCR normalization is vital. This study found that PCR efficiency significantly impacts reference gene validation, urging careful validation of new software tools.

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Area of Science:

Molecular Biology
Genomics

Background:

Accurate normalization is essential for real-time reverse transcription polymerase chain reaction (RT-qPCR) data analysis.
Identifying and validating stable reference genes is crucial as no single gene remains consistently expressed across different conditions or cell types.
Several algorithms exist for validating optimal reference genes.

Purpose of the Study:

To compare the performance of three main reference gene validation algorithms against the RefFinder tool.
To assess reference gene stability in human testicular tissue and Sertoli cell line samples.
To evaluate the impact of PCR efficiency on reference gene validation outcomes.

Main Methods:

Real-time reverse transcription polymerase chain reaction (RT-qPCR) was performed on 14 candidate reference genes.
Gene expression stability was analyzed using geNorm, NormFinder, and BestKeeper algorithms.
Data were analyzed using original software packages and the RefFinder tool.

Main Results:

geNorm, NormFinder, and BestKeeper yielded similar rankings for the most and least stable genes.
R-based packages (NormqPCR, SLqPCR, NormFinder for R) provided identical gene rankings.
RefFinder produced different results compared to original software when using raw Cq values, suggesting bias due to unconsidered PCR efficiencies.

Conclusions:

Assay efficiency is a critical parameter in reference gene validation for RT-qPCR.
New software tools for reference gene validation require thorough validation before implementation.
The RefFinder tool's output may be biased if PCR efficiencies are not accounted for.