Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
Jo Vandesompele,Katleen De Preter,Filip Pattyn,Bruce Poppe,Nadine Van Roy,Anne De Paepe,Franki Speleman +6 more
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TLDR
The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which opens up the possibility of studying the biological relevance of small expression differences.Abstract:
Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.read more
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The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments
Stephen A. Bustin,Vladimir Benes,Jeremy A. Garson,Jan Hellemans,Jim F. Huggett,Mikael Kubista,Reinhold Mueller,Tania Nolan,Michael W. Pfaffl,Gregory L. Shipley,Jo Vandesompele,Carl T. Wittwer,Carl T. Wittwer +12 more
TL;DR: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency.
Journal ArticleDOI
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
Todd Z. DeSantis,Philip Hugenholtz,Neils Larsen,Mark Rojas,Eoin L. Brodie,Keith Keller,Thomas Huber,Daniel Dalevi,Ping Hu,Gary L. Andersen +9 more
TL;DR: A 16S rRNA gene database (http://greengenes.lbl.gov) was used to provide chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies as mentioned in this paper.
Journal ArticleDOI
Obesity is associated with macrophage accumulation in adipose tissue
Stuart P. Weisberg,Daniel McCann,Manisha Desai,Michael Rosenbaum,Rudolph L. Leibel,Anthony W. Ferrante +5 more
TL;DR: Transcript expression in perigonadal adipose tissue from groups of mice in which adiposity varied due to sex, diet, and the obesity-related mutations agouti (Ay) and obese (Lepob) found that the expression of 1,304 transcripts correlated significantly with body mass.
Journal ArticleDOI
Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets
TL;DR: A novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes, rooted in a mathematical model of gene expression, that provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene.
Journal ArticleDOI
Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper--Excel-based tool using pair-wise correlations.
TL;DR: The developed, and herein presented, software called BestKeeper determines the best suited standards, out of ten candidates, and combines them into an index, which can be compared with further target genes to decide, whether they are differentially expressed under an applied treatment.
References
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