REST 2005

version 1.9.6  released November 2005
version 1.9.9  released December 2005
version 1.9.12  released   April 2006
The new stand-alone software versions REST 2005 was programmed and designed by Matthew Herrmann, David Chiew, working at Corbett Research (Sydney, Australia) and Michael W. Pfaffl, Technical University of Munich, Germany.

Download =>  Manual  REST 2005  ( updated in April 2006 )

Download  =>  REST 2005 software  (link removed)

Please download newest version REST 2009

REST 2005 is a new standalone software tool to estimate up and down-regulation for gene expression studies. The software addresses issues surrounding the measurement of uncertainty for expression ratios, by using randomisation and bootstrapping techniques. By increasing the number of iterations from 2,000 to 50,000 in this version hypothesis tests achieve a level of consistency on par with traditional statistical tests. New confidence intervals for expression levels also allow scientists to measure not only the statistical significance of deviations, but also their likely magnitude, even in the presence of outliers. Graphical output of the data via a whisker box-plots provide a visual representation of variation for each gene that highlights potential issues such as distribution skew.

Why REST 2005 ?
Prior to REST (Relative Expression Software Tool, Pfaffl et al 2002), Relative Quantitation in qRT-PCR was a technique which allowed the estimation of gene expression. While useful, it did not provide statistical information suitable for comparing groups of treated versus untreated samples in a robust fashion. To illustrate with an example, let us say we are testing to see if a particular mRNA is responsible for sending pain messages. We split up our patients into two groups: one which will be subject to pain (such immersion of the hand in ice-cold water), and the other, which is our control group. Following this, we measure the quantities of gene of interest mRNA in both groups, relative to reference genes. Our question is: did the group subject to pain release more gene of interest mRNA than the other? Prior approaches are insufficient to answer this question. They may calculate an average expression value indicating whether a particular subject in one group appeared to release more or less gene of interest mRNA than another subject, but without any statistical test to determine accuracy. Due to the use of ratios in gene expression, it becomes very complex to perform traditional statistical analysis, as ratio distributions do not have a standard deviation. REST 2005 overcomes these problems by using simple statistical randomisation tests. Such tests can appear counter-intuitive and so it is recommended to read the discussions on randomisation techniques in the topic Links before continuing.

Reference Gene Normalisation
REST 2005 is more comprehensive than prior techniques, as it takes multiple reference genes into consideration when determining expression. When estimating a sample's expression ratio, an intermediate absolute concentration value is calculated according to the following formula:

concentration = efficiencyavg(Controls) – avg(Samples)

This formula is used to obtain mean estimates of the uncorrected absolute concentration for each
gene. For a single reference gene, the concentration of the gene of interest is divided by the reference gene value to obtain an expression level, as is done in the Two Standard Curve technique:

expression = goiConcentration ÷ refConcentration

For multiple reference genes, the geometric mean is taken of all reference gene concentrations, since
concentration estimates vary exponentially (Vandesompele et al., 2002):

expression = goiConcentration ÷ GEOMEAN (refConc1, refConc2,, …)

Alternatively, to normalise according to multiple reference genes, a second approach can be used, to normalise the individual expressions
relative to each reference gene which represents an alternative approximations of the true expression value. To take all into account simultaneously, they are averaged using a geometric mean (since ratios are being used):

expression = GEOMEAN(goiConcentration ÷ refConc1, goiConcentration ÷ refConc2, …)

Since the mean concentrations of each gene do not change, they can be calculated at the beginning of
the algorithm, and expressed as a single value, called the "Normalisation factor", equal to their geometric mean.

Greater Accuracy for Hypothesis Tests
The redevelopment of the REST 2005 software as a stand-alone application provides an order of magnitude of increase in performance. The speed improvements have been used to increase the number of randomisation iterations from 2,000 to 50,000, compared to earlier REST versions (Pfaffl et al., NAR 2002), increasing the accuracy and reproducibility of hypothesis tests to a level equivalent to traditional statistical tests.

Expression Level Confidence Intervals
While previous REST publications provide a means of determining the mean output and a P value for the likelihood of up or down-regulation using a hypothesis test, bootstrapping techniques can be used to provide 95% confidence intervals for expression ratios, without normality or symmetrical distribution assumptions. While a hypothesis test provides a measure of whether there was a statistically significant result, the confidence interval provides a range that can be checked for semantic significance. For example, drinking cough medicine before driving may increase the chances of an accident by 1x10^-6 %. While a statistical test may show the difference to be significant, it clearly poses no real threat to drivers, when taking into consideration the average number of accidents a driver has in their lifetime.

Efficiency Error Measurement
All statistical tests in REST 2005 now include correction for variation in efficiency. If variation in efficiency is low, hypothesis tests will produce more conclusive results, and confidence bands for estimated expression will be smaller. As all statistics are calculated using randomisation techniques, the approach for measuring standard curve error must also be stochastic, and is expressed as a challenge: If we ignore variation in the standard curve, the slope (m value) will be expressed as a constant in all equations. Say, then, we have a standard curve of six data points for the gene GAPDH that we use to estimate its efficiency. If there is no variation in the standard curve, then we could pick any two points in the curve and still measure the same gradient. If, however, there is large variation between the points, then random selection of points will greatly vary the efficiency calculated. Using a few data points, we can then simulate the random variable representing the efficiency error. The randomised efficiency value is then included in calculations instead of the slope of the line of best fit, feeding any variation in efficiency directly into the relative quantitation hypothesis tests and confidence intervals.

Whisker-Box Plots
REST 2005 replaces the bar graph visualisation in prior versions with a statistical whisker-box plot. In statistical applications, whisker-box plots provide additional information about the skew of distributions that would not be available simply by plotting the sample mean. See the link below for general information about whisker-box plots:


"Relative Expression Software Tool (REST) for group-wise comparison and statistical analysis of relative expression results in Real-Time PCR", (Pfaffl et al, 2002)

"Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple
internal control genes" (Vandersompele et al, 2002)

"Bootstrap Methods and their Application" (A.C. Davidson, D.V. Hinkley 2002), Cambridge University

Rotor-Gene Software User Guide (Corbett Life Science)

This reference provides a good introduction to the philosophy of randomised tests:

This reference provides an online interactive example of the test:

This reference provides more detailed descriptions on how to carry out traditional tests, such as
determination of confidence intervals and hypothesis testing using bootstrapping and randomisation:

A description of Whisker-Box Plots:

Contact Information

Please find more information in the HELP manual of the REST 2005 software (by pressing F1) or at
the respective web page on the pages:

Obtain software updates to REST 2005 here:

If you have further questions or comments to improve the software, your suggestion are always
Please contact us at this address:

Corbett Research:

Page 2:   CP data import  &  PCR efficiency calculation

Page 3:    Result page

Page 4:   Whisker Plot

Page 5:   Graphical output

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