Reliable gene expression analysis by reverse transcription-quantitative PCR: reporting and minimizing the uncertainty in data accuracy

Reverse transcription-quantitative PCR (RT-qPCR) has been widely adopted to measure differences in mRNA levels; however, biological and technical variation strongly affects the accuracy of the reported differences. RT-qPCR specialists have warned that, unless researchers minimize this variability, t...

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Vydáno v:	The Plant cell Ročník 26; číslo 10; s. 3829
Hlavní autoři:	Remans, Tony, Keunen, Els, Bex, Geert Jan, Smeets, Karen, Vangronsveld, Jaco, Cuypers, Ann
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 01.10.2014
Témata:	Arabidopsis - genetics Arabidopsis Proteins - genetics Gene Expression Profiling - methods Gene Expression Profiling - standards Gene Expression Profiling - statistics & numerical data Gene Expression Regulation, Plant Humans Reference Standards Reproducibility of Results Research Design - standards Reverse Transcriptase Polymerase Chain Reaction - methods Reverse Transcriptase Polymerase Chain Reaction - standards Reverse Transcriptase Polymerase Chain Reaction - statistics & numerical data Uncertainty
ISSN:	1532-298X, 1532-298X
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Shrnutí:	Reverse transcription-quantitative PCR (RT-qPCR) has been widely adopted to measure differences in mRNA levels; however, biological and technical variation strongly affects the accuracy of the reported differences. RT-qPCR specialists have warned that, unless researchers minimize this variability, they may report inaccurate differences and draw incorrect biological conclusions. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines describe procedures for conducting and reporting RT-qPCR experiments. The MIQE guidelines enable others to judge the reliability of reported results; however, a recent literature survey found low adherence to these guidelines. Additionally, even experiments that use appropriate procedures remain subject to individual variation that statistical methods cannot correct. For example, since ideal reference genes do not exist, the widely used method of normalizing RT-qPCR data to reference genes generates background noise that affects the accuracy of measured changes in mRNA levels. However, current RT-qPCR data reporting styles ignore this source of variation. In this commentary, we direct researchers to appropriate procedures, outline a method to present the remaining uncertainty in data accuracy, and propose an intuitive way to select reference genes to minimize uncertainty. Reporting the uncertainty in data accuracy also serves for quality assessment, enabling researchers and peer reviewers to confidently evaluate the reliability of gene expression data.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1532-298X 1532-298X
DOI:	10.1105/tpc.114.130641