Relative Expression
Software Tool = REST
Today the "relative gene
expression approach" is increasingly used in gene expression
studies, where the expression of a target gene is standardised by a nonregulated
referencegene or by an index, containing more
referencegenes. Several mathematical algorithm have been developed to compute the
expression ratio, based on realtime PCR efficiency and the crossing point (Ct or CP)
deviation (=> delta CP) of an unknown sample versus a control. But
all
published equations and
available models for the calculation of relative expression ratio
allow only for the determination of a single
transcription difference
between one control and one sample.
Therefore new software
tools were established, named REST
© (Relative Expression Software Tool),
which compare two
or more treatments groups
or conditions (in RESTMCS), with up to 100 data points in sample or control
group (in RESTXL),
for multiple
reference genes and up to 15 target genes (in
REST384) .
The mathematical model used is based on the
correction for exact PCR
efficiencies
and the mean crossing point deviation between
sample group(s) and
control group(s). Subsequently the expression ratio results of the investigated transcripts are
tested for significance by a Pair
Wise Fixed
Reallocation Randomisation Test © and plotted using standard
error (SE) estimation via a complex Taylor
algorithm
(=> see below).
REST
software
versions (REST384,
RESTRG and RESTMCS)
were established in collaboration with:
 Y.
Vainshtein,
EMBL,
Gene Expression Unit, M. Hentze Group, Germany;
 P. Avery, School of Mathematics and Statistics,
University of Newcastle, UK;
 G.
Horgan,
Biomathematics and Statistics Scotland, Rowett Research Institute,
Scotland;
 M.
W. Pfaffl, Physiology Weihenstephan, Technical University of
Munich, Germany;
Standalone
software versions REST2005
& REST2008 were programmed
and
designed by:
 M. Herrmann, D. Chiew, B. Speller Corbett
Research, Sydney, Australia
 M. W.
Pfaffl, Physiology Weihenstephan,
Technical University of
Munich, Germany
Standalone
software versions REST2009
was programmed
and
designed by:
 Qiagen, Hilden, Germany => http://www.REST.de.com
 M. W.
Pfaffl, Physiology Weihenstephan,
Technical University of
Munich, Germany
Many
thanks
to all coworkers !
New REST
software applications
are available:
Relative
Expression
Software Tool (REST©) for
group wise comparison
and statistical analysis of
relative expression results in realtime PCR.
Michael W.
Pfaffl Graham W. Horgan & Leo Dempfle
Nucleic Acids
Research 2002 May 1; 30(9): E36
Realtime
reverse transcription followed by polymerase chain reaction (RTPCR)
is the most
suitable method for the detection and quantification of mRNA. It offers
high sensitivity,
good reproducibility, and a wide quantification range. Today relative expression is increasingly
used, where the expression of a target gene is standardised by a non regulated reference
gene. Several mathematical algorithm
have been developed
to compute an expression ratio, based on realtime
PCR efficiency and the crossing point deviation
of an unknown sample versus
a control. But all published equations and
available models for the
calculation of relative expression ratio allow only for the determination of a single
transcription difference between one control and one sample. Therefore a new software tool
was established, named REST © (Relative Expression Software Tool), which compares
two groups, with up to 16 data points in sample and 16 in control
group, for reference and up to four target genes. The mathematical model used is based on the PCR
efficiencies and the mean crossing point deviation between sample and control
group. Subsequently the expression ratio results of the four investigated transcripts
are tested for significances by a randomisation test. Herein development and
application of REST is explained and the usefulness of relative expression in
realtime
PCR using REST is discussed.
REST
ratio error
estimation
using
Taylor series
implemented in the REST application software versions REST 384
Real
Time PCR: A
useful new
approach? Statistical
Problems?
by Peter Avery, School of Mathematics
and Statistics, University of Newcastle, UK
