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Related Concept Videos

Real Time RT-PCR02:57

Real Time RT-PCR

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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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A simple spreadsheet-based method for relative quantification using quantitative real-time PCR.

Hien Fuh Ng1, Yun Fong Ngeow1

  • 1Centre for Research on Communicable Diseases, Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia.

Biochemistry and Molecular Biology Education : a Bimonthly Publication of the International Union of Biochemistry and Molecular Biology
|December 2, 2021
PubMed
Summary
This summary is machine-generated.

We developed a simple spreadsheet method for gene expression relative quantification using quantitative real-time PCR (qPCR). This tool simplifies complex calculations, making gene expression analysis accessible for researchers.

Keywords:
gene expression studiesquantitative real time PCRrelative quantificationspreadsheet

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genetics

Background:

  • Quantitative real-time PCR (qPCR) is widely used for gene expression studies.
  • Current relative quantification methods involve complex calculations, posing challenges for many researchers.
  • A need exists for simplified, accessible tools for analyzing qPCR data.

Purpose of the Study:

  • To develop an easy-to-use, spreadsheet-based method for relative gene expression quantification.
  • To simplify the complex calculation steps involved in qPCR data analysis.
  • To provide a transparent and understandable tool for molecular biology researchers.

Main Methods:

  • Developed a spreadsheet tool accepting gene efficiencies and Cq values as input.
  • Implemented step-by-step normalization, fold change calculation, and statistical analysis.
  • Validated the method using four diverse published qPCR datasets.

Main Results:

  • The spreadsheet method successfully performed normalization, fold change calculation, and statistical analysis.
  • Results obtained using this method were concordant with those from the REST 2009 software.
  • The tool demonstrated ease of use and understanding of the underlying calculations.

Conclusions:

  • The developed spreadsheet method offers a simplified approach to relative gene expression quantification via qPCR.
  • This tool requires no specialized software or expertise, benefiting students, educators, and scientists.
  • The method enhances accessibility and understanding of gene expression data analysis in molecular biology.