Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper – Excel-based tool using pair-wise correlations

By M. W. Pfaffl, Ales Tichopád, C. Prgomet, T. Neuvians. Biotechnology Letters, 2004

3,923 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8b46c6e2adf7f4dd5184cb6c47e7fffc8d24b198

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Citations in the corpus

Evaluation of potential reference genes in real-time RT-PCR studies of Atlantic salmon (2005, methodology): “In general, the stability of several potential reference genes should be tested in every examined tissue or cell, and under different experimental design [5, 6 ].”
The effect of substrate stiffness on adult neural stem cell behavior. (2009, background): “efficiency corrected calculation model for multiple reference genes [33].”
Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR (2009, methodology): “Several statistical methods for evaluating reference genes were proposed such as geNorm (Vandesompele et al. 2002), Norm Finder (Andersen et al. 2004), and Best Keeper (Pfaffl et al. 2004).”
Reference genes in real-time PCR (2013, methodology): “A popular tool for validation of reference genes is also BestKeeper, a software developed by Pfaffl et al. (2004).”
Exosomal miR-1290 and miR-375 as Prognostic Markers in Castration-resistant Prostate Cancer (2014, methodology): “Selected miRNAs were further processed by NormFinder [16] and BestKeeper [17].”
An improvement of the 2ˆ(-delta delta CT) method for quantitative real-time polymerase chain reaction data analysis. (2013, background): “However, variations in the expression of these genes may depend on species, tissue types, disease conditions, and other potential factors (28).”
Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues. (2008, methodology): “This paradox has been addressed using pairwise measures (Vandesompele et al. 2002) and model-based estimates of expression variation (Andersen et al. 2004), among other approaches (Pfaffl et al. 2004; Szabo et al. 2004).”
Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators (2011, methodology): “It is now assumed that the correct reference genes should be selected by evaluating data from expression studies with the available statistical algorithms, such as geNorm (Vandesompele et al. 2002), Bestkeeper (Pfaffl et al. 2004) or Normfinder (Andersen et al. 2004).”
The real-time polymerase chain reaction. (2006, background): “htm), and softwares have been developed to find the optimal reference genes for defined systems (Pfaffl et al., 2004; Vandesompele et al., 2002).”
RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization (2011, methodology): “Three of the most popular algorithms are GeNorm [13], Norm finder [8] and Bestkeeper [15].”
qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data (2007, methodology): “While different tools are available to determine which candidate reference genes are stably expressed (for example, geNorm [8,13], BestKeeper [16], Normfinder [17]), almost no software is available to perform straightforward normalization using more than one reference gene (with the exception of the”
Reference Gene Selection for Insect Expression Studies Using Quantitative Real-Time PCR: The Head of the Honeybee, Apis mellifera, After a Bacterial Challenge (2008, methodology): “Statistical algorithms such as geNorm (Vandesompele et al. 2002), Normfinder (Andersen et al. 2004) and BestKeeper (Pfaffl et al. 2004) have been developed to assess the appropriateness of reference genes.”