Article

qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data.

Center for Medical Genetics, Ghent University Hospital, De Pintelaan, B-9000 Ghent, Belgium.
Genome biology (Impact Factor: 10.47). 02/2007; 8(2):R19. DOI: 10.1186/gb-2007-8-2-r19
Source: PubMed

ABSTRACT Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.

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Questions & Answers about this publication

  • Jo Vandesompele added an answer in Real-Time PCR:
    How can I determine standard error of control sample in real-time qPCR when there is always value 1?
    The final value after normalisation (LightCycler 480) in control sample is always 1. How can I determine error? If I count it by myself (Pffafl equation) it is stil 1 in control sample. What am I doing wrong?
    Thanks a lot.
    Jo Vandesompele · Ghent University
    All our formulas for error propagation during qPCR data-analysis are mentioned in attached paper. The formulas are integrated in Biogazelle's qbase+ software (http://www.qbaseplus.com). I recommend to analyse all samples at the same time in the software. qbase+ is compatible with LC480 output.
  • Jo Vandesompele added an answer in Gene Expression:
    Can anyone help with calculating error in RT-qPCRs fold-change data?
    I'm considering some real time data on tissues treated and untreated upon a given stress. Obviously based on three technical replicate in each condition I obtained a normalized expression value associated with a standard error based on the replicates. If I calculate the log2 of the fold change (treated / untreated) how can I calculate the corresponding error bars?
    Jo Vandesompele · Ghent University
    All formulas for error propagation during qPCR data-analysis are mentioned in attached paper. The formulas are integrated in Biogazelle's qbase+ software (http://www.qbaseplus.com).
  • Paul Oladimeji added an answer in PCR:
    Does anyone know what the ideal proportions of cDNA and primer are for preparing master mix in qPCR?
    I've got so many recipes and I am confused which one is best. However, I tried one which apparently did not work and I could just see the amplification for my cyclophillin not for target genes. I tried 0.25 ul forward and 0.25 reverse primer and 1.5 ul cDNA for total 15 ul reaction.
    Paul Oladimeji · PrimerDesign Ltd.
    Hi Saeideh,

    The old Reverse Transcription (RT) efficiency problem!

    Firstly I would caution against any spectrophotometric analysis (Nanodrop or Picodrop etc.) of “cDNA” because in most cases it’s not just cDNA, it’s the reverse transcription (RT) reaction containing cDNA. So as your colleagues described, measurements will be compromised by the reaction constituents such as free dNTPs.

    Instead, it’s probably best to run all of the relevant RNA samples of a particular experiment simultaneously, in the same RT thermocycle, preparing samples with a single RT reaction cocktail.

    Additionally, if you’re performing absolute quantification, it is essential to standardise your RNA concentrations beforehand using RNase-free water (NOT DEPC-treated water, because DEPC can interfere with PCR). It’s best practice to do this when performing relative quantification too.

    After that, we come to the assumption of 100% reverse transcription efficiency. Here is where most labs differ. I was taught to repeat the RT reactions on the full set of samples so I would have 2 RT reactions for each sample. I could then pool each sample’s separate RT reaction into 1 representative RT mix. However, I’ve also worked in labs where only 1 RT reaction was used.

    Essentially you’re assuming a 1:1 conversion (or 100% RT reaction efficiency). However because your samples are prepared and run together, any deficiencies in the RT are assumed to affect all samples equally. This allows you to accurately compare mRNA transcript levels accurately across your samples.

    As qPCR quantity estimates (even absolute) are ratios, either to a reference gene or a sample with known concentration/copy number, what is most important is to maintain uniformity across all samples. You can confirm success of the RT reaction with a positive control.

    Using qPCR to compare samples prepared by different RT reactions introduces confounding factors which are extremely difficult to control for (i.e. RT cocktail variability and differences in thermocycle performance). I would advise against any experimental workflow that requires you to do this unless you have read the attached paper, describing the use of inter-run calibration (IRC).

    On a final note, if you wanted to persist with using a nanodrop/picodrop to analyse the RT reaction, I’ve heard researchers describe using RT negative controls as blanks in spectrophotometry with some success. However, I have no personal experience of this.

    Good luck!
    Paul
  • Jo Vandesompele added an answer in Real-Time PCR:
    How to deal with conflicting results that occur at normalisation to three different reference genes?
    I am currently calculating relative expression values "by hand", using the following equation: Ct(1+Efficiency)^(-Ct). I then normalize these data to the respective reference gene Cts and finally calculate the ratio treated/control. My problem: in roughly half of the cases the expression of the gene of interest looks drastically different, depending on what ref. gene I used.
    I chose my ref. genes based on a microarray study specific for my experiment plus two online tools that predict the behaviour of these genes under various treatments.
    Jo Vandesompele · Ghent University
    With respect to the black box discussion... Biogazelle's qbase+ software is entirely based on peer-reviewed and open access algorithms and formulas (Hellemans et al., Genome Biology, 2007; Vandesompele et al., Genome Biology, 2002).
    To answer the question of conflicting results when normalizing with gene A, B or C. First, make sure all 3 are stably expressed (using one of the many published algorithms, e.g. geNorm). Second, using the geometric mean of the relative quantities of all 3 reference genes will do a much better job for normalization (remove better the experimentally induced variation). Third, also consider the magnitude and statistical significance of the fold-change; the conflicting results may be marginal differences.
  • Jo Vandesompele added an answer in Real-Time PCR:
    Do I have to run 2 housekeeping genes (18S and beta-actin) together in each run in a Real-time PCR?
    I'm running Real-time PCR on STEPONEplus instrument (singleplex) which allows me to choose only one endogenous gene. However, can I run another endogenous gene in the same 96 well? I would also want to know if I have to run these 2 endogenous gene every time I'm running Real Time or can I use 1 endogenous gene and the other in different sets of experiments but use the values from these sets to calculate the CT values?
    Jo Vandesompele · Ghent University
    When doing relative quantification, there is no need that your references genes are measured in the same plate (run) as your genes of interest. It is perfectly okay to measure gene of interest in plate 1, and measure reference gene 1 in plate 2, and reference gene 2 in plate 3 (see Figure 2 - sample maximization strategy in attached paper).
  • Jo Vandesompele added an answer in Real-Time PCR:
    qPCR normalisation
    Do you know if it is possible to normalise samples across different qPCR plates? I have many samples to analyse and it is not possible to get them all on one plate.
    Jo Vandesompele · Ghent University
    The procedure is explained in this paper.