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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
Price: GB £150 or US $310 (hardback).
Pages: x + 284 (plus colour plates)

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.

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

pyQPCR

http://pyqPCR.sourceforge.net/

pyQPCR is an open-source software. It can be used to perform qPCR analysis. It may be used, copied and modified with no restriction according to the GPLv3 (or higher) licence.

pyQPCR is a GUI application written in python that deals with quantitative PCR (QPCR) raw data. Using quantification cycle values extracted from QPCR instruments, it uses a proven and universally applicable model to give finalized quantification results.

Import QPCR raw data / open existing file

During this first step, you can:

Create a new project: you give a project name, choose the PCR device (for now only Eppendorf ones are supported, but others can be easily added) and import your raw data (TXT or CSV files) of one or several plates. Some examples of these files are given with the source of pyQPCR.
Open an existing one: pyQPCR has its own file format which is XML based. You can directly open these files (examples are in the source code of pyQPCR).

At any time, you can add or remove a plate from your project thanks to the corresponding icons.

Plate settings

You can edit the data of each well separately or select and modify a group of wells. You also can change the targets and samples properties (name, efficiency of the primers), and remove or add new ones. You can disable wells in order to not take them into account for calculations.

Standard curve calculation

You can define as "standards" the wells that contains dilutions of DNA in order to calculate PCR efficiency. Then, you precise the amount of DNA (arbitrary unit) in the different wells and the program will plot the standard curve and calculate PCR efficiency for this set of primers. This efficiency will be taken into account for subsequent relative quantifications.

Reference target and sample

For relative quantification calculations, you must define a reference gene and target. They can be either shared for all plates or specific of each plate.

Relative quantification

The wells defined as "unknown" are used to calculate relative quantifications. An improved ΔΔCt method allows you to obtain reliable quantifications and error. The confidence level is modifiable and can be either gaussian or calculated using a T-test. The program plots results as histograms that are easy to customize.

Results, export and save

Results can be printed or exported in a pdf file containing a table with all the data and plots for standard curves and/or relatives quantifications. You can also save your project in the pyQPCR XML file format that allows you to keep the entire project with the different plates and settings easely recoverable.

qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J. Genome Biol. 2007;8(2): R19.

qBASE Talk at the qPCR 2007 symposium

The qpcR library - Analysis of real-time PCR data using R

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:

Fit sigmoidal (three-, four- and five-parameter) models to the raw fluorescence data.
Calculate essential PCR parameters (efficiency, threshold cycles, initial template fluorescence F0) from the sigmoidal fits and display comprehensive graphics.
Conduct a model selection process in which the best sigmoidal model is chosen by nested F-tests on the residual variance.
Derive values from more classical quantitation methods, such as the ‘window-of-linearity’ method, exponential fitting of the identified exponential region or a calibration curve from diluted samples.
In calibration curve analysis, find the threshold fluorescence value which maximizes the linearity of the dilution curve 'threshold cycles'.
Further optimize the fitting process by eliminating cycles in the ground and plateau phase, using all possible combinations.
Calculate many measures for the goodness-of-fit, such as the residual variance, R-squared, the Akaike Information Criterion (AIC), the corrected AIC (AICc), the root-mean-squared-error (RMSE) and Allen's PRESS statistic.
Do a batch analysis of many runs with all methods (this often reveals dramatic differences in the estimated parameters!).
Predict either fluorecence or cycle values from data.
Calculate the goodness-of-fit (by means of RMSE) of 14 different sigmoidal models within the exponential region of the qPCR curve.
Conduct gaussian error propagation with Monte Carlo simulation using multivariate normal distributions if a covariance matrix is given.
Calculate ratios and their propagated errors for all combinations of qPCR runs, using single or replicated data. If reference PCRs are supplied, the ratios are normalized against these. Additionally, t-tests on the crossing points/efficiencycrossing points can be conducted.
Graphical display of all sample/replicate combinations ratios using propagated/standard errors and results from the t-tests.
Build an averaged model from several housekeeping PCRs.
Calculate model selection measures such as Likelihood Ratios (nested) or Akaike weights (non-nested).
Calculate the Cy0 value as described in Guescini et al.
Do a maxRatio analysis as in Shain et al.
Analyze your data with an R implementation of the popular REST software.
Screenshots => http://www.dr-spiess.de/qpcR/screen/screen.html

PowerNest - illuminating error in qPCR experiment design

PowerNest is a software tool enabling experimenters to explore the effect of sampling on noise propagation throughout qPCR assays. The sampling process is assumed to be comprised of a number of levels; the acquisition of a sample and the preparation of extracted material, reverse-transcription of the mRNA, and the qPCR itself. Given a small set of data, representative of a larger assay, the error at each stage of the experiment is profiled using a nested-ANOVA.
Armed with this information, PowerNest allows the experimenter to explore the effects of modifications to the experimental design on the expected total error of the assay. When given the financial cost of replicates at each level, PowerNest will calculate a cost-optimal sampling-plan, delivering an experiment design that will minimise processing error and maximise the statistical resolution of the assay.

The software is temporarily undergoing final testing, during which time it has been made available as a free download.
http://www.powernest.net/

PowerNest Poster

Real-Time PCR: A Review of Approaches to Data Analysis.