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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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LEMming: A Linear Error Model to Normalize Parallel Quantitative Real-Time PCR (qPCR) Data as an Alternative to

Ronny Feuer¹, Sebastian Vlaic², Janine Arlt³

¹Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.

|September 2, 2015

Summary

A new method, LEMming, offers reference gene-independent normalization for parallel quantitative real-time PCR (qPCR), overcoming biases from traditional methods. This approach improves data accuracy by avoiding reliance on stable reference genes (RGs).

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Linear Amplification Mediated PCR – Localization of Genetic Elements and Characterization of Unknown Flanking DNA

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Published on: June 25, 2014

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Quantitative real-time PCR (qPCR) is a gold standard for gene expression analysis.
Parallelized qPCR enables high-throughput transcript abundance quantification.
Existing normalization methods like geNorm and ΔΔCt rely on stable reference genes (RGs), which can introduce bias.

Purpose of the Study:

To develop a reference gene-independent normalization method for parallel qPCR data.
To overcome biases associated with traditional RG-dependent normalization techniques.
To improve the accuracy and reliability of gene expression data analysis.

Main Methods:

Developed LEMming, a novel normalization approach based on a tailored linear error model for parallel qPCR data.
Utilized the assumption that mean Ct values within similarly treated groups are equal.
Evaluated LEMming's performance on three diverse datasets and compared it with geNorm normalization.

Main Results:

LEMming provided similar results to geNorm when stable RGs were present.
LEMming-normalized data showed no mean shift per gene per condition, unlike geNorm-normalized data in datasets with unstable RGs.
LEMming demonstrated superior performance in reducing data variability compared to geNorm.

Conclusions:

Traditional RG stability criteria can fail when RGs are coexpressed and condition-dependent.
LEMming effectively resolves technical and biological biases in qPCR by eliminating RG dependency.
Quantifying total cDNA content can aid in identifying systematic errors for enhanced data interpretation.