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Related Concept Videos

Real Time RT-PCR02:57

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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Selection of optimal reference genes for normalization in quantitative RT-PCR.

Inna Chervoneva1, Yanyan Li, Stephanie Schulz

  • 1Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu

BMC Bioinformatics
|May 18, 2010
PubMed
Summary

Selecting optimal reference genes for quantitative real-time PCR (qRT-PCR) normalization is crucial. This study introduces a robust method to identify the best reference gene subsets, minimizing variability and improving accuracy, especially when gene variations are correlated.

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Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs
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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Quantitative real-time PCR (qRT-PCR) requires normalization to account for experimental variations.
  • Traditional normalization using single or multiple reference genes can introduce variability due to inherent gene expression differences.
  • Existing methods often assume independence of reference gene variations, potentially leading to suboptimal gene set selection.

Purpose of the Study:

  • To develop a robust approach for selecting optimal reference gene subsets for qRT-PCR normalization.
  • To minimize the variance of normalizing factors by considering all possible correlations among candidate reference genes.
  • To provide flexibility in selection criteria, optimizing for minimal variability, minimal gene number, or average rank.

Main Methods:

  • Proposed a method to estimate normalizing factor variance using the unstructured covariance matrix of candidate reference genes.
  • Employed bootstrapping to ensure robustness and calculate upper confidence limits for log-transformed normalizing factor variances.
  • Evaluated all possible gene subsets of varying sizes, unlike previous methods focusing on individual gene stability.

Main Results:

  • Identified reference gene subsets with lower empirical variance in normalizing factors across multiple datasets compared to existing methods.
  • Demonstrated improved sensitivity in identifying optimal reference gene subsets, particularly when candidate genes exhibit modest or negative correlations.
  • The approach successfully identified subsets with smaller normalizing factor variability, outperforming standard methods.

Conclusions:

  • The proposed method offers a comprehensive and robust evaluation of reference gene subset variability.
  • It provides flexibility in choosing optimal subsets based on various criteria, addressing limitations of current approaches.
  • This approach is particularly advantageous in scenarios with non-trivial innate correlations among candidate reference genes.