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BioInformatics content - further pages:
Big biological datasets map life's
networks -- Multi-omics offers a new way of
doing biology.
Michael
Snyder’s
genes were telling him that he might be at
increased risk for type 2 diabetes. The Stanford
University geneticist wasn’t worried: He felt
healthy and didn’t have a family history of the
disease. But as he monitored other aspects of
his own biological data over months and years,
he saw that diabetes was indeed emerging, even
though he showed no symptoms.
Snyder’s story illustrates the power of looking beyond the genome, the complete catalog of an organism’s genetic information. His tale turns the genome’s one-dimensional view into a multidimensional one. In many ways, a genome is like a paper map of the world. That map shows where the cities are. But it doesn’t say anything about which nations trade with each other, which towns have fierce football rivalries or which states will swing for a particular political candidate. Open one of today’s digital maps, though, and numerous superimposed data sources give a whole lot of detailed, real-time information. With a few taps, Google Maps can show how to get across Boston at rush hour, offer alternate routes around traffic snarls and tell you where to pick up a pizza on the way. Now, scientists like Snyder are developing these same sorts of tools for biology, with far-reaching consequences. To figure out what’s really happening within an organism — or within a particular organ or cell — researchers are linking the genome with large-scale data about the output of those genes at specific times, in specific places, in response to specific environmental pressures. While the genome remains mostly stable over time, other “omes” change based on what genes are turned on and off at particular moments in particular places in the body. The proteome (all an organism’s proteins) and the metabolome (all the metabolites, or small molecules that are the outputs of biological processes) are two of several powerful datasets that become more informative when used together in a multi-omic approach. They show how that genomic instruction manual is actually being applied. “The genome tells you what can happen,” says Oliver Fiehn, a biochemist at the University of California, Davis. The proteome and the metabolome can show what’s actually going on. And just as city planners use data about traffic patterns to figure out where to widen roads and how to time stoplights, biologists can use those entwined networks to predict at a molecular level how individual organisms will respond under specific conditions. By linking these layers and others to expand from genomics to multi-omics, scientists might be able to meet the goals of personalized medicine: to figure out, for example, what treatment a particular cancer patient will best respond to, based on the network dynamics responsible for a tumor. Or predict whether an experimental vaccine will work before moving into expensive clinical tests. Or help crops grow better during a drought. And while many of those applications are still in the future, researchers are laying the groundwork right now. “Biology is being done in a way that’s never been done before,” says Nitin Baliga, director of the Institute for Systems Biology in Seattle. Original
Publication:
Trans-Omics --
How To Reconstruct Biochemical Networks
Across Multiple ‘Omic’ Layers
Katsuyuki
Yugi,
Hiroyuki Kubota, Atsushi Hatano, Shinya Kuroda
Trends in Biotechnology 2016 34(4): 276-290 We propose 'trans-omic' analysis for reconstructing global biochemical networks across multiple omic layers by use of both multi-omic measurements and computational data integration. We introduce technologies for connecting multi-omic data based on prior knowledge of biochemical interactions and characterize a biochemical trans-omic network by concepts of a static and dynamic nature. We introduce case studies of metabolism-centric trans-omic studies to show how to reconstruct a biochemical trans-omic network by connecting multi-omic data and how to analyze it in terms of the static and dynamic nature. We propose a trans-ome-wide association study (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic and environmental factors, which can characterize several complex lifestyle diseases as breakdowns in the trans-omic system.  Bioinformatics Made Easy Search bioinformatics tools and run genomic analysis in the cloud https://insidedna.me/ We are
excited to invite you to beta test of InsideDNA
platform which provide:
How it works?
Currently, our service is free and we are thrilled to provide 10 Gb of storage space and 10 compute credits to each new user. These 10 credits roughly equal to 260 hours of computational work on different compute nodes*. We hope that you will be pleasantly surprised by how much analysis you can do during these hours. In addition, if you fill our entry survey, we will give you an extra 10 compute credits. The survey aims to make InsideDNA application better and more user friendly. How will it work in the future? While we are trying to make this service as affordable as possible for researchers, compute nodes are provided to us by a third party and we can only keep current service free of charge for several months and for limited number of users. After that we will have to charge for computing with a price of $10 USD per 10 compute credits (~260 hours of work). We only deduct credits when you actually do the analysis - not when you are idle. Bug, errors and problems Despite we have been testing InsideDNA for several months internally, it is still likely to have bugs. Thus, we kindly ask you to report any issues or problems you may experience with InsideDNA. Please provide any feedback to this email: InsideDNA@gmail.com Next releases and forthcoming features Currently we are working on more exciting features including provisioning of a much bigger storage space for each user. Vote for different features in our application to get them done quicker or talk to us and suggest other features which you think may be useful! Enjoy happy sequence crunching with InsideDNA!  GenEx offers advanced methods to analyze real-time qPCR data with simple clicks of the mouse GenEx is a popular software for qPCR data processing and analysis. Built in a modular fashion GenEx provides a multitude of functionalities for the qPCR community, ranging from basic data editing and management to advanced cutting-edge data analysis. Basic data editing and management Arguably the most important part of qPCR experiments is to pre-process the raw data into shape for subsequent statistical analyses. The pre-processing steps need to be performed consistently in correct order and with confidence. GenEx standard’s streamlined and user-friendly interface ensures mistake-free data handling. Intuitive and powerful presentation tools allow professional illustrations of even the most complex experimental designs. Advanced cutting-edge data analysis When you need more advanced analyses GenEx 6 is the product for you. Powerful enough to demonstrate feasibility it often proves sufficient for most users demands. Current features include parametric and non-parametric statistical tests, Principal Component Analysis, and Artificial Neural Networks. New features are continuously added to GenEx with close attention to customers’ needs. New features Sample handling and samples individual biology often contribute to confounding experimental variability. By using the new nested ANOVA feature in GenEx a user will be able to evaluate variance contributions from each step in the experimental procedure. With a good knowledge of the variance contributions, an appropriate distribution of experimental replicates can be selected to minimize confounding variance and maximize the power of the experimental design! For experiments with complex features, such as for example multifactorial diseases, analytical relationships and classifications may not readily be available. The support vector machine feature in the new version of GenEx is so easy to use that it will make this advanced supervised classification method easily available to novice users, while providing access to advanced parameters for experts. The methods are suitable to select and validate reference genes, classify samples, group genes, monitor time dependent processes and much more. Please see the GenEx web page or Online Tutorials 
Learn more - For further information of the analyses in GenEx, see the GenEx online help manual or www.qPCRforum.com A survey of
tools for the analysis of quantitative PCR
(qPCR) data
Stephan Pabinger, Stefan Rödiger, Albert Kriegner, Klemens Vierlinger, Andreas Weinhäusel Biomolecular Detection and Quantification 1 (2014) 23–33 Real-time
quantitative polymerase-chain-reaction
(qPCR) is a standard technique in most
laboratories used for various applications
in basic research. Analysis of qPCR data is
a crucial part of the entire experiment,
which has led to the development of a
plethora of methods. The released tools
either cover specific parts of the workflow
or provide complete analysis solutions.
Here, we surveyed 27 open-access software
packages and tools for the analysis of qPCR
data. The survey includes 8 Microsoft
Windows, 5 web-based, 9 R-based and 5 tools
from other platforms. Reviewed packages and
tools support the analysis of different qPCR
applications, such as RNA quantification,
DNA methylation, genotyping, identification
of copy number variations, and digital PCR.
We report an overview of the functionality,
features and specific requirements of the
individual software tools, such as data
exchange formats, availability of a
graphical user interface, included
procedures for graphical data presentation,
and offered statistical methods. In
addition, we provide an overview about
quantification strategies, and report
various applications of qPCR. Our
comprehensive survey showed that most tools
use their own file format and only a
fraction of the currently existing tools
support the standardized data exchange
format RDML. To allow a more streamlined and
comparable analysis of qPCR data, more
vendors and tools need to adapt the
standardized format to encourage the
exchange of data between instrument
software, analysis tools, and researchers.
  For each tool its corresponding application area is specified, divided into: Cq calculation, normalization, quantification, CNV, and dPCR. The input type can either be precalculated Cq values (Cq) or raw fluorescence values (Raw). For each tool the supported operating system or the underlying framework is specified. Frameworks are often available on different operating systems allowing the package to run on several platforms. GUI specifies the existence of a graphical user interface for data input and output. ABI, Applied Biosystems format; ABT, Lightcycler export format; CSV, comma separates values, FLO, Lightcycler export format; REX, Rotor Gene export format; R format, encompasses all import and export formats provided by the default R installation and auxiliary R packages (e.g., PDF, SVG, HTML, and XLS). On non-detects
in qPCR data.
McCall MN, McMurray HR, Land H, Almudevar A Bioinformatics. 2014 Aug 15;30(16): 2310-2316 MOTIVATION:
Quantitative real-time PCR (qPCR) is one of
the most widely used methods to measure gene
expression. Despite extensive research in
qPCR laboratory protocols, normalization and
statistical analysis, little attention has
been given to qPCR non-detects-those
reactions failing to produce a minimum
amount of signal.
RESULTS: We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project. Reverse
transcription quantitative real-time PCR
(RT-qPCR) is a key method for measurement
of relative gene expression. Analysis of
RT-qPCR data requires many iterative
computations for data normalization and
analytical optimization. Currently no
computer program for RT-qPCR data analysis
is suitable for analytical optimization
and user-controllable customization based
on data quality, experimental design as
well as specific research aims. Here I
introduce an all-in-one computer program,
SASqPCR,
for robust and rapid analysis of RT-qPCR
data in SAS. This program has multiple
macros for assessment of PCR efficiencies,
validation of reference genes,
optimization of data normalizers,
normalization of confounding variations
across samples, and statistical comparison
of target gene expression in parallel
samples. Users can simply change the macro
variables to test various analytical
strategies, optimize results and customize
the analytical processes. In addition, it
is highly automatic and functionally
extendable. Thus users are the actual
decision-makers controlling RT-qPCR data
analyses. SASqPCR and its tutorial are
freely available at http://code.google.com/p/sasqpcr/downloads/list
Determinants
of expression variability
Alemu EY, Carl JW Jr, Corrada Bravo H, Hannenhalli S Nucleic Acids Res. 2014 Apr;42(6): 3503-3514 The
amount of tissue-specific expression
variability (EV) across individuals is an
essential characteristic of a gene and
believed to have evolved, in part, under
functional constraints. However, the
determinants and functional implications
of EV are only beginning to be
investigated. Our analyses based on
multiple expression profiles in 41 primary
human tissues show that a gene's EV is
significantly correlated with a number of
features pertaining to the genomic,
epigenomic, regulatory, polymorphic,
functional, structural and network
characteristics of the gene. We found that
(i) EV of a gene is encoded, in part, by
its genomic context and is further
influenced by the epigenome; (ii) strong
promoters induce less variable expression;
(iii) less variable gene loci evolve under
purifying selection against copy number
polymorphisms; (iv) genes that encode
inherently disordered or highly
interacting proteins exhibit lower
variability; and (v) genes with less
variable expression are enriched for
house-keeping functions, while genes with
highly variable expression tend to
function in development and extra-cellular
response and are associated with human
diseases. Thus, our analysis reveals a
number of potential mediators as well as
functional and evolutionary correlates of
EV, and provides new insights into the
inherent variability in eukaryotic gene
expression.
 Select the right Reference gene
with Genevestigator
Genevestigator is a high quality and manually curated expression database and meta-analysis system. It allows biologists to study the expression and regulation of genes in a broad variety of contexts by summarizing information from hundreds of microarray experiments into easily interpretable results. A user-friendly interface allows you to visualize gene expression in many different tissues, at multiple developmental stages, or in response to large sets of stimuli, diseases, drug treatments, or genetic modifications. This type of meta-analysis is core to understanding the spatio-temporal-response regulation of genes, to identify or validate biomarkers, and to find out which subnetworks are commonly affected in different diseases and conditions. www.genevestigator.com
Screenshots Video Tutorials Graphical
user interface. The different
tools are presented as icons and grouped
by tool sets. The Genevestigator tools
help you to find relevant conditions for
your genes of interest, to find genes
having special properties (e.g.
biomarkers), or to identify gene
expression modules that are co-regulated
over selected conditions. The tools let
you analyze individual experiments or
thousands of experiments simultaneously.
 RefGenes tool. Identification of genes having the smallest expression variance across 26,075 human samples (Affymetrix 133 Plus 2 arrays). The two boxplots in the upper section represent, as a comparison, the expression distribution of PPIA and B2M (two commonly used reference genes for RT-qPCR) across the same set of samples. => RefGenes tutorial ExpressionData - A public resource of high quality curated datasets representing gene expression across anatomy, development and experimental conditions. Zimmermann P, Bleuler S, Laule O, Martin F, Ivanov NV, Campanoni P, Oishi K, Lugon-Moulin N, Wyss M, Hruz T, Gruissem W. BioData Min. 2014 7: 18 -- eCollection 2014. Reference datasets are often used
to compare, interpret or validate experimental
data and analytical methods. In the field of
gene expression, several reference datasets
have been published. Typically, they consist
of individual baseline or spike-in experiments
carried out in a single laboratory and
representing a particular set of conditions.
Here, we describe a new type of standardized
datasets representative for the spatial and
temporal dimensions of gene expression. They
result from integrating expression data from a
large number of globally normalized and
quality controlled public experiments.
Expression data is aggregated by anatomical
part or stage of development to yield a
representative transcriptome for each
category. For example, we created a
genome-wide expression dataset representing
the FDA tissue panel across 35 tissue types.
The proposed datasets were created for human
and several model organisms and are publicly
available at http://www.expressiondata.org
A multilevel gamma-clustering layout algorithm for visualization of biological networks. Hruz T, Wyss M, Lucas C, Laule O, von Rohr P, Zimmermann P, Bleuler S. Adv Bioinformatics. 2013: 920325 Visualization of large complex
networks has become an indispensable part of
systems biology, where organisms need to be
considered as one complex system. The
visualization of the corresponding network is
challenging due to the size and density of
edges. In many cases, the use of standard
visualization algorithms can lead to high
running times and poorly readable
visualizations due to many edge crossings. We
suggest an approach that analyzes the
structure of the graph first and then
generates a new graph which contains specific
semantic symbols for regular substructures
like dense clusters. We propose a multilevel
gamma-clustering layout visualization
algorithm (MLGA) which proceeds in three
subsequent steps: (i) a multilevel γ
-clustering is used to identify the structure
of the underlying network, (ii) the network is
transformed to a tree, and (iii) finally, the
resulting tree which shows the network
structure is drawn using a variation of a
force-directed algorithm. The algorithm has a
potential to visualize very large networks
because it uses modern clustering heuristics
which are optimized for large graphs.
Moreover, most of the edges are removed from
the visual representation which allows keeping
the overview over complex graphs with dense
subgraphs.
Global regulatory architecture of human, mouse and rat tissue transcriptomes. Prasad A, Kumar SS, Dessimoz C, Bleuler S, Laule O, Hruz T, Gruissem W, Zimmermann P. BMC Genomics. 2013 14: 716 BACKGROUND: Predicting
molecular responses in human by extrapolating
results from model organisms requires a
precise understanding of the architecture and
regulation of biological mechanisms across
species.
RESULTS:
Here, we present a large-scale comparative
analysis of organ and tissue transcriptomes
involving the three mammalian species human,
mouse and rat. To this end, we created a
unique, highly standardized compendium of
tissue expression. Representative tissue
specific datasets were aggregated from more
than 33,900 Affymetrix expression microarrays.
For each organism, we created two expression
datasets covering over 55 distinct tissue
types with curated data from two independent
microarray platforms. Principal component
analysis (PCA) revealed that the
tissue-specific architecture of transcriptomes
is highly conserved between human, mouse and
rat. Moreover, tissues with related biological
function clustered tightly together, even if
the underlying data originated from different
labs and experimental settings. Overall, the
expression variance caused by tissue type was
approximately 10 times higher than the
variance caused by perturbations or diseases,
except for a subset of cancers and chemicals.
Pairs of gene orthologs exhibited higher
expression correlation between mouse and rat
than with human. Finally, we show evidence
that tissue expression profiles, if combined
with sequence similarity, can improve the
correct assignment of functionally related
homologs across species.
CONCLUSION:
The results demonstrate that
tissue-specific regulation is the main
determinant of transcriptome composition and
is highly conserved across mammalian species.
Investigation of variation in gene expression profiling of human blood by extended principle component analysis. Xu Q, Ni S, Wu F, Liu F, Ye X, Mougin B, Meng X, Du X. Fudan University Shanghai Cancer Center - Institut Mérieux Laboratory, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China. PLoS One. 2011;6(10): e26905 BACKGROUND: Human peripheral blood
is a promising material for biomedical
research. However, various kinds of biological
and technological factors result in a large
degree of variation in blood gene expression
profiles.
METHODOLOGY/PRINCIPAL FINDINGS:
Human peripheral blood samples were drawn from
healthy volunteers and analysed using the
Human Genome U133Plus2 Microarray. We applied
a novel approach using the Principle Component
Analysis and Eigen-R(2) methods to dissect the
overall variation of blood gene expression
profiles with respect to the interested
biological and technological factors. The
results indicated that the predominating
sources of the variation could be traced to
the individual heterogeneity of the relative
proportions of different blood cell types
(leukocyte subsets and erythrocytes). The
physiological factors like age, gender and BMI
were demonstrated to be associated with 5.3%
to 9.2% of the total variation in the blood
gene expression profiles. We investigated the
gene expression profiles of samples from the
same donors but with different levels of RNA
quality. Although the proportion of variation
associated to the RNA Integrity Number was
mild (2.1%), the significant impact of RNA
quality on the expression of individual genes
was observed.
CONCLUSIONS: By characterizing the
major sources of variation in blood gene
expression profiles, such variability can be
minimized by modifications to study designs.
Increasing sample size, balancing confounding
factors between study groups, using rigorous
selection criteria for sample quality, and
well controlled experimental processes will
significantly improve the accuracy and
reproducibility of blood transcriptome study.
 Download free version of GenEx software ! Multi dimensional qPCR data analysis via GenEx analysis software (MultiD) Real-time PCR
gene expression profiling
Mikael Kubista, Björn Sjögreen, Amin Forootan, Radek Sindelka and Jiri Jonák, and José Manuel Andrade Real-time PCR has rapidly become the preferred technique for quantitative analysis of nucleic acids. Its superior sensitivity, reproducibility and dynamic range make it the preferred choice for expression profiling in scientific, as well as routine, applications. => Link to GenEx software Real-Time PCR: Current Technology and Applications http://www.horizonpress.com/realtimepcr
Publisher: Caister Academic Press Editor: Julie Logan, Kirstin Edwards and Nick Saunders Applied and Functional Genomics, Health Protection Agency, London (2009) ISBN: 978-1-904455-39-4 Chapter 4 - Reference Gene Validation Software for Improved Normalization J. Vandesompele, M. Kubista and M. W. Pfaffl (2009) Real-time PCR is the method of choice for expression analysis of a limited number of genes. The measured gene expression variation between subjects is the sum of the true biological variation and several confounding factors resulting in non-specific variation. The purpose of normalization is to remove the non-biological variation as much as possible. Several normalization strategies have been proposed, but the use of one or more reference genes is currently the preferred way of normalization. While these reference genes constitute the best possible normalizers, a major problem is that these genes have no constant expression under all experimental conditions. The experimenter therefore needs to carefully assess whether a certain reference gene is stably expressed in the experimental system under study. This is not trivial and represents a circular problem. Fortunately, several algorithms and freely available software have been developed to address this problem. This chapter aims to provide an overview of the different concepts. Chapter 5 - Data Analysis Software M. W. Pfaffl, J. Vandesompele and M. Kubista (2009) Quantitative real-time RT-PCR (qRT-PCR) is widely and increasingly used in any kind of mRNA quantification, because of its high sensitivity, good reproducibility and wide dynamic quantification range. While qRT-PCR has a tremendous potential for analytical and quantitative applications, a comprehensive understanding of its underlying principles is important. Beside the classical RT-PCR parameters, e.g. primer design, RNA quality, RT and polymerase performances, the fidelity of the quantification process is highly dependent on a valid data analysis. This review will cover all aspects of data acquisition (trueness, reproducibility, and robustness), potentials in data modification and will focus particularly on relative quantification methods. Furthermore useful bioinformatical, biostatical as well as multi-dimensional expression software tools will be presented. Real-Time
PCR: Current Technology and
Applications - Book reviews:
"... a useful book for students ..." from J. Microbiological Methods "provides a dual focus by
aiming, in the early chapters, to provide both
the theory and practicalities of this diverse
and superficially simple technology,
counter-balancing this in the later chapters
with real-world applications, covering
infectious diseases, biodefence, molecular
haplotyping and food standards." from
Microbiology Today
"a reference work that should be
found both in university libraries and on the
shelves of experienced applications
specialists." from Microbiology
Today
"a comprehensive guide to real-time PCR technology and its applications" from Food Science and Technology Abstracts (2009) Volume 41 Number 6 "This volume should be of utmost
interest to all investigators interested and
involved in using RT-PCR ... the RT-PCR
protocols covered in this book will be of
interest to most, if not all, investigators
engaged in research that uses this important
technique ... a well balanced book covering
the many potential uses of real-time PCR ...
valuable for all those interested in RT-PCR."
from Doodys reviews (2009)
"provide the novice and the
experienced user with guidance on the
technology, its instrumentation, and its
applications" f rom SciTech Book News
2009 p. 64
"... written by international authors expert in specific technical principles and applications ... a useful compendium of basic and advanced applications for laboratory scientists. It is an ideal introductory textbook and will serve as a practical handbook in laboratories where the technology is employed." from Christopher J. McIver, Microbiology Department, Prince of Wales Hospital, New South Wales, Australia writing in Australian J. Med. Sci. 2009. 30(2): 59-60
Statistical
analysis of real-time PCR data.
Yuan JS, Reed A, Chen F, Stewart CN Jr. BMC Bioinformatics. 2006 (7): 85. Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA. BACKGROUND: Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data. RESULTS: In the first approach, a multiple regression analysis model was developed to derive DeltaDeltaCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the DeltaDeltaCt can be derived from analysis of effects of variables. The other two models involve calculation DeltaCt followed by a two group t-test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS. CONCLUSION: Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR. Data
Analysis
Methods
There are two methods, both equally valid, for analyzing data obtained from real time PCR: Relative Standard Curve Method and Comparative CT Method. The first, relative standard curve method, is useful for investigators that have a limited number of cDNA samples and a large number of genes of interest. The comparative CT method is useful for investigators who have a lage number of cDNA samples and a limited number of genes of interest (RRC Core Genomics Facility, University of Illinois at Chicago) qPCR
Bioinformatik: Neue Entwicklungen in
der post-qPCR Datenanalyse (in German)
Michael W. Pfaffl (2006), Laborwelt (1): 10-13, ISSN 1611–0854 (Editor: T. Gabrielczyk) Die Entwicklung der Polymerase Ketten Reaktion (PCR) in den 80er Jahren gehört zweifelsohne zu den größten Errungenschaften in der Molekularbiologie. Mittels der klassischen PCR lassen sich hochsensitiv Genabschnitte oder DNA Fragmente qualitativ sowie semi-quantitativ nachweisen. Um spezifische mRNA zu quantifizieren, stellt man der PCR die Reverse Transkription (RT) vor. Die Anwendung der RT-PCR zur Quantifizierung spezifischen mRNA ist heute zum Routinewerkzeug in der Expressionsanalytik geworden. Die gewonnenen Ergebnisse sind von überproportionalen Nutzen in der molekularbiologischen Forschung und molekularen Diagnostik, in der vergleichenden Expressionsanalytik sowie zur Aufklärung der „Functional Genomics“. Der Nachweis kann qualitativ in klassischen Thermocyclern oder in „real-time“ quantitativ mittels Echtzeit PCR (qPCR) durchgeführt werden. Die Ergebnisse sind direkt verfügbar, so dass der Einsatz der qPCR eine deutliche Zeitersparnis mit sich bringt. Da die Zunahme der Fluoreszenz und die Menge an neusynthetisierten PCR-Produkten über einen weiten Bereich proportional zueinander sind, kann aus den gewonnenen Fluoreszenzdaten die eingesetzte Ausgangsmenge der DNA respektive RNA bestimmt werden. Vorraussetzung für einen zuverlässigen quantitativen Nachweis ist eine funktionierende Analytik und Datenauswertung, die exakte Quantifizierungsergebnisse bei ausreichender Genauigkeit und hoher Wiederholbarkeit liefert. QPCR DEMO - real-time PCR data management
and analysis
Developed by - Stephan Pabinger http://genome.tugraz.at/QPCR or https://esus.genome.tugraz.at/rtpcr QPCR is a versatile web-based Java application that allows to store, manage, analyze, and display data from quantitative real-time polymerase chain reaction (qPCR) experiments. You can try out the application by using the demo account at QPCR Demo It is strongly recommended to use a private account which guarantees confidentiality and security of your data. To request an account please contact qpcr@genome.tugraz.at To get started: Read the tutorial which leads you through all important steps of the application. For more information download the user guide which covers all aspects of the application. QPCR:
Application for real-time PCR data
management and analysis.
Pabinger S, Thallinger GG, Snajder R, Eichhorn H, Rader R, Trajanoski Z. BMC Bioinformatics 2009, 10:268 BACKGROUND: Since its introduction quantitative real-time polymerase chain reaction (qPCR) has become the standard method for quantification of gene expression. Its high sensitivity, large dynamic range, and accuracy led to the development of numerous applications with an increasing number of samples to be analyzed. Data analysis consists of a number of steps, which have to be carried out in several different applications. Currently, no single tool is available which incorporates storage, management, and multiple methods covering the complete analysis pipeline. RESULTS: QPCR is a versatile web-based Java application that allows to store, manage, and analyze data from relative quantification qPCR experiments. It comprises a parser to import generated data from qPCR instruments and includes a variety of analysis methods to calculate cycle-threshold and amplification efficiency values. The analysis pipeline includes technical and biological replicate handling, incorporation of sample or gene specific efficiency, normalization using single or multiple reference genes, inter-run calibration, and fold change calculation. Moreover, the application supports assessment of error propagation throughout all analysis steps and allows conducting statistical tests on biological replicates. Results can be visualized in customizable charts and exported for further investigation. CONCLUSION: We have developed a web-based system designed to enhance and facilitate the analysis of qPCR experiments. It covers the complete analysis workflow combining parsing, analysis, and generation of charts into one single application. The system is freely available at http://genome.tugraz.at/QPCR 
The qpcR library is an extension to the R environment that assists in the modelling and analysis of quantitative real-time PCR data => http://www.dr-spiess.de/qpcR.html With the qpcR library you can:
 PowerNest - illuminating error in qPCR experiment design
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