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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Quantitative real-time RT-PCR data analysis: current concepts and the novel “gene expression’s CT difference” formula
TL;DR: The issue of real-time PCR data analysis and its mathematical background is focused on, offering a general concept for efficient, fast and precise data analysis superior to the commonly used comparative CT and the standard curve method, as it considers individual amplification efficiencies for every PCR.
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Normalization of microRNA expression levels in quantitative RT-PCR assays: Identification of suitable reference RNA targets in normal and cancerous human solid tissues
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GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues
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Lifestyle transitions in plant pathogenic Colletotrichum fungi deciphered by genome and transcriptome analyses
Richard J. O'Connell,Michael R. Thon,Stéphane Hacquard,Stefan G. Amyotte,Jochen Kleemann,Maria F. Torres,Ulrike Damm,Ester Alvarenga Santos Buiate,Lynn Epstein,Noam Alkan,Janine Altmüller,Lucia Alvarado-Balderrama,Christopher Bauser,Christian Becker,Bruce W. Birren,Zehua Chen,Jaeyoung Choi,Jo Anne Crouch,Jonathan P. Duvick,Jonathan P. Duvick,Mark A. Farman,Pamela Gan,David I. Heiman,Bernard Henrissat,Richard J. Howard,Mehdi Kabbage,Christian Koch,Barbara Kracher,Yasuyuki Kubo,Audrey D. Law,Marc-Henri Lebrun,Yong-Hwan Lee,Itay Miyara,Neil Moore,Ulla Neumann,Karl Nordström,Daniel G. Panaccione,Ralph Panstruga,Ralph Panstruga,Michael Place,Robert H. Proctor,Dov Prusky,Gabriel E. Rech,Richard Reinhardt,Jeffrey A. Rollins,Steve Rounsley,Christopher L. Schardl,David C. Schwartz,Narmada Shenoy,Ken Shirasu,Usha Rani Sikhakolli,Kurt Stüber,Serenella A. Sukno,James A. Sweigard,Yoshitaka Takano,Hiroyuki Takahara,Hiroyuki Takahara,Frances Trail,H. Charlotte van der Does,H. Charlotte van der Does,Lars M. Voll,Isa Will,Sarah Young,Qiandong Zeng,Jingze Zhang,Shiguo Zhou,Martin B. Dickman,Paul Schulze-Lefert,Emiel Ver Loren van Themaat,Li-Jun Ma,Li-Jun Ma,Lisa J. Vaillancourt +71 more
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Reference genes in real-time PCR.
Bartłomiej Kozera,Marcin Rapacz +1 more
TL;DR: Various aspects of the use of reference genes in qPCR technique used in the thousands of present studies are discussed, including examples of best normalizing genes in some specific cases and possible mistakes are presented based on available sources.
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