Data Analysis and BioInformatics in realtime qPCR (5) => latest papers <= main page subpage 1 subpage 2 subpage 3 subpage 4  integrative data analysis subpage 5  latest paper updates Molecular Regulatory Networks Big Data in Transcriptomics & Molecular Biology
Latest papers: MAKERGAUL  an innovative MAK2based model and software for realtime PCR quantification Bultmann CA and Weiskirchen R Clin Biochem. 2014 47(12): 117122 OBJECTIVES: Gene
expression analysis by quantitative PCR is a standard
laboratory technique for RNA quantification with high
accuracy. In particular realtime PCR techniques using
SYBR Green and melting curve analysis allowing
verification of specific product amplification have
become a well accepted laboratory technique for rapid
and high throughput gene expression quantification.
However, the software that is applied for
quantification is somewhat circuitous and needs
actually above average manual operation.
DESIGN AND METHODS: We here developed a novel,
simple to handle open source software package (i.e.,
MAKERGAUL) for quantification of gene expression data
obtained by real time PCR technology.RESULTS: The
developed software was evaluated with an already well
characterized real time PCR data set and the
performance parameters (i.e., absolute bias,
linearity, reproducibility, and resolution) of the
algorithm that are the basis of our calculation
procedure compared and ranked with those of other
implemented and wellestablished algorithms. It shows
good quantification performance with reduced
requirements in computing power.
CONCLUSIONS: We
conclude that MAKERGAUL is a convenient and easy to
handle software allowing accurate and fast expression
data analysis.
Highlights
Comparing realtime quantitative polymerase chain reaction analysis methods for precision, linearity, and accuracy of estimating amplification efficiency Tellinghuisen J, Spiess AN Anal Biochem. 2014 Mar 15;449:7682 New methods are
used to compare seven qPCR analysis methods for their
performance in estimating the quantification cycle
(Cq) and amplification efficiency (E) for a large test
data set (94 samples for each of 4 dilutions) from a
recent study. Precision and linearity are assessed
using chisquare (χ(2)), which is the minimized
quantity in leastsquares (LS) fitting, equivalent to
the variance in unweighted LS, and commonly used to
define statistical efficiency. All methods yield Cqs
that vary strongly in precision with the starting
concentration N0, requiring weighted LS for proper
calibration fitting of Cq vs log(N0). Then χ(2) for
cubic calibration fits compares the inherent precision
of the Cqs, while increases in χ(2) for quadratic and
linear fits show the significance of nonlinearity.
Nonlinearity is further manifested in unphysical
estimates of E from the same Cq data, results which
also challenge a tenet of all qPCR analysis methods 
that E is constant throughout the baseline region.
Constantthreshold (Ct) methods underperform the other
methods when the data vary considerably in scale, as
these data do.
A new method for quantitative realtime polymerase chain reaction data analysis Rao X, Lai D, Huang X. J Comput Biol. 2013 20(9): 703711 Quantitative
realtime polymerase chain reaction (qPCR) is a
sensitive gene quantification method that has been
extensively used in biological and biomedical fields.
The currently used methods for PCR data analysis,
including the threshold cycle method and linear and
nonlinear modelfitting methods, all require
subtracting background fluorescence. However, the
removal of background fluorescence can hardly be
accurate and therefore can distort results. We propose
a new method, the takingdifference linear regression
method, to overcome this limitation. Briefly, for each
two consecutive PCR cycles, we subtract the
fluorescence in the former cycle from that in the
latter cycle, transforming the n cycle raw data into
n1 cycle data. Then, linear regression is applied to
the natural logarithm of the transformed data.
Finally, PCR amplification efficiencies and the
initial DNA molecular numbers are calculated for each
reaction. This takingdifference method avoids the
error in subtracting an unknown background, and thus
it is more accurate and reliable. This method is easy
to perform, and this strategy can be extended to all
current methods for PCR data analysis.
The choice of reference gene affects statistical efficiency in quantitative PCR data analysis Guo Y, Pennell ML, Pearl DK, Knobloch TJ, Fernandez S, Weghorst CM. Biotechniques. 2013 55(4): 207209 Quantitative
polymerase chain reaction (qPCR), a highly sensitive
method of measuring gene expression, is widely used in
biomedical research. To produce reliable results, it
is essential to use stably expressed reference genes
(RGs) for data normalization so that sampletosample
variation can be controlled. In this study, we examine
the effect of different RGs on statistical efficiency
by analyzing a qPCR data set that contains 12 target
genes and 3 RGs. Our results show that choosing the
most stably expressed RG for data normalization does
not guarantee reduced variance or improved statistical
efficiency. We also provide a formula for determining
when data normalization will improve statistical
efficiency and hence increase the power of statistical
tests in data analysis.
Eprobe mediated realtime PCR monitoring and melting curve analysis Hanami T, Delobel D, Kanamori H, Tanaka Y, Kimura Y, Nakasone A, Soma T, Hayashizaki Y, Usui K, Harbers M. PLoS One. 2013 Aug 7;8(8):e70942 Realtime
monitoring of PCR is one of the most important methods
for DNA and RNA detection widely used in research and
medical diagnostics. Here we describe a new approach
for combined realtime PCR monitoring and melting
curve analysis using a 3' endblocked
ExcitonControlled Hybridizationsensitive fluorescent
Oligonucleotide (ECHO) called Eprobe. Eprobes contain
two dye moieties attached to the same nucleotide and
their fluorescent signal is strongly suppressed as
singlestranded oligonucleotides by an excitonic
interaction between the dyes. Upon hybridization to a
complementary DNA strand, the dyes are separated and
intercalate into the doublestrand leading to strong
fluorescence signals. Intercalation of dyes can
further stabilize the DNA/DNA hybrid and increase the
melting temperature compared to standard DNA
oligonucleotides. Eprobes allow for specific realtime
monitoring of amplification reactions by hybridizing
to the amplicon in a sequencedependent manner.
Similarly, Eprobes allow for analysis of reaction
products by melting curve analysis. The function of
different Eprobes was studied using the L858R mutation
in the human epidermal growth factor receptor (EGFR)
gene, and multiplex detection was demonstrated for the
human EGFR and KRAS genes using Eprobes with two
different dyes. Combining amplification and melting
curve analysis in a singletube reaction provides
powerful means for new mutation detection assays.
Functioning as "sequencespecific dyes", Eprobes hold
great promises for future applications not only in PCR
but also as hybridization probes in other
applications.
BootstRatio: A webbased statistical analysis of foldchange in qPCR and RTqPCR data using resampling methods Clèries R1, Galvez J, Espino M, Ribes J, Nunes V, de Heredia ML. Comput Biol Med. 2012 42(4): 438445 Realtime
quantitative polymerase chain reaction (qPCR) is
widely used in biomedical sciences quantifying its
results through the relative expression (RE) of a
target gene versus a reference one. Obtaining
significance levels for RE assuming an underlying
probability distribution of the data may be difficult
to assess. We have developed the webbased application
BootstRatio, which tackles the statistical
significance of the RE and the probability that
RE>1 through resampling methods without any
assumption on the underlying probability distribution
for the data analyzed. BootstRatio perform these
statistical analyses of gene expression ratios in two
settings: (1) when data have been already normalized
against a control sample and (2) when the data control
samples are provided. Since the estimation of the
probability that RE>1 is an important feature for
this type of analysis, as it is used to assign
statistical significance and it can be also computed
under the Bayesian framework, a simulation study has
been carried out comparing the performance of
BootstRatio versus a Bayesian approach in the
estimation of that probability. In addition, two
analyses, one for each setting, carried out with data
from real experiments are presented showing the
performance of BootstRatio. Our simulation study
suggests that Bootstratio approach performs better
than the Bayesian one excepting in certain situations
of very small sample size (N≤12). The web application
BootstRatio is accessible through
http://regstattools.net/br and developed for the
purpose of these intensive computation statistical
analyses.
RTqPCR workflow for singlecell data analysis Anders Ståhlberg, Vendula Rusnakova, Amin Forootan, Miroslava Anderova, Mikael Kubista Methods 2013, Vol 59, Issue 1, pages 8088 Individual cells
represent the basic unit in tissues and organisms and
are in many aspects unique in their properties. The
introduction of new and sensitive techniques to study
singlecells opens up new avenues to understand
fundamental biological processes. Well established
statistical tools and recommendations exist for gene
expression data based on traditional cell population
measurements. However, these workflows are not
suitable, and some steps are even inappropriate, to
apply on singlecell data. Here, we present a simple
and practical workflow for preprocessing of
singlecell data generated by reverse transcription
quantitative realtime PCR. The approach is
demonstrated on a data set based on profiling of 41
genes in 303 singlecells. For some preprocessing
steps we present options and also recommendations. In
particular, we demonstrate and discuss different
strategies for handling missing data and scaling data
for downstream multivariate analysis. The aim of this
workflow is provide guide to the rapidly growing
community studying singlecells by means of reverse
transcription quantitative realtime PCR profiling.
Evaluation of qPCR curve analysis methods for reliable biomarker discovery  bias, resolution, precision, and implications Ruijter JM1, Pfaffl MW, Zhao S, Spiess AN, Boggy G, Blom J, Rutledge RG, Sisti D, Lievens A, De Preter K, Derveaux S, Hellemans J, Vandesompele J. Methods. 2013 59(1): 3246 RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RTqPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycleaxis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) [11]. The original developers of these methods applied their algorithms and are coauthor on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patientclassification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the algorithms' precision, bias, and resolution. While large differences exist between methods when considering the technical performance experiments, most methods perform relatively well on the biomarker data. The data and the analysis results per method are made available to serve as benchmark for further development and evaluation of qPCR curve analysis methods. Download data => http://qPCRDataMethods.hfrc.nl

