Data
Analysis and
BioInformatics in
realtime qPCR (5) 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

