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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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High-Throughput Quantitative RT-PCR in Single and Bulk C. elegans Samples Using Nanofluidic Technology

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Published on: May 28, 2020

Data-driven normalization strategies for high-throughput quantitative RT-PCR.

Jessica C Mar¹, Yasumasa Kimura, Kate Schroder

¹Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu

BMC Bioinformatics

|April 21, 2009

Summary

This summary is machine-generated.

Normalization is crucial for gene expression profiling using quantitative reverse transcriptase polymerase chain reaction (qPCR). Data-driven methods, particularly quantile normalization, offer robust alternatives to traditional housekeeping gene approaches for large datasets.

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High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs

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Published on: August 3, 2011

Area of Science:

Molecular Biology
Bioinformatics

Background:

Quantitative reverse transcriptase polymerase chain reaction (qPCR) is essential for gene expression profiling.
High-throughput qPCR generates large datasets requiring robust normalization.
Accurate data preprocessing is critical for reliable gene expression analysis.

Purpose of the Study:

To develop and evaluate data-driven normalization methods for high-throughput qPCR data.
To provide robust alternatives to standard housekeeping gene normalization techniques.
To address the challenge of technical variation in gene expression profiling.

Main Methods:

Development of two novel data-driven normalization algorithms.
Evaluation of methods against a single housekeeping gene approach.
Implementation of methods in the R package qpcrNorm, available via Bioconductor.

Main Results:

Data-driven normalization methods effectively correct for technical variation.
Quantile normalization demonstrated superior performance compared to single housekeeping gene methods.
The proposed methods offer robust normalization for large-scale qPCR experiments.

Conclusions:

Data-driven normalization strategies are highly effective for large qPCR datasets.
These methods are particularly valuable when experimental conditions affect housekeeping genes.
The qpcrNorm R package provides accessible tools for advanced qPCR data normalization.