REST-384 beta version 2 [ August 2006 ]
REST-RG beta software version 3 [ August 2006 ]=> download here: rest-rg-beta-9august2006.zip
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.
Management and automated analysis of real-time quantitative PCR data
Introduction Gene expression analysis is becoming increasingly important in biological research and clinical decision making, with real-time quantitative PCR becoming the method of choice for expression profiling of selected genes. Maturation of chemistry and hardware has made the practical performance of real-time quantitative PCR measurements feasible for most laboratories. However, accurate and straightforward mathematical and statistical analysis of the raw data (cycle threshold values) as well as the management of growing data sets have become the major hurdles in gene expression analyses. Since the software provided along with the different detection systems does not provide an adequate solution for these issues, we developed qBase, a free software program for the management and automated analysis of real-time quantitative PCR data.
What is qBase ? qBase is a collection of macros for Microsoft Excel (currently only Windows version) for the management and automated analysis of real-time quantitative PCR data. The program employs a delta-Ct relative quantification model with PCR efficiency correction and multiple reference gene normalization. The qBase Browser allows data storage and annotation by hierarchically organizing real-time PCR runs into projects > experiments > runs. It is compatible with the export files from many currently available PCR instrument softwares and provides easy access to all your data, both raw and processed. The qBase Analyzer contains an easy run (plate) editor, performs quality control and inter-plate calibration, converts Ct values into normalized and rescaled quantities with proper error propagation, and displays results both tabulated and in graphs. The program can handle an unlimited number of samples, genes and replicates, and allows data from multiple runs to be processed together (preceded by an inter-run calibration if required). The possibility to use up to 5 reference genes allows reliable and robust normalization of gene expression levels. qBase allows easy exchange of data between users, and exports tabulated data for further statistical analyses using other dedicated software.
Other qPCR related tools form our group
geNorm expression stability analysis of candidate reference genes for accurate normalization
[Vandesompele et al., Genome Biology, 2002]
RTPrimerDB real-time PCR primer and probe database with currently 3439 real-time PCR assays
[Pattyn et al., Nucleic Acids Research, 2003]
Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis
Download Q-Gene software
Quantitative real-time PCR represents a highly sensitive and powerful technique for the quantitation of nucleic acids. It has a tremendous potential for the high-throughput analysis of gene expression in research and routine diagnostics. However, the major hurdle is not the practical performance of the experiments themselves but rather the efficient evaluation and the mathematical and statistical analysis of the enormous amount of data gained by this technology, as these functions are not included in the software provided by the manufacturers of thedetection systems. In this work, we focus on the mathematical evaluation and analysis of the data generated by quantitative real-time PCR, the calculation of the final results, the propagation of experimental variation of the measured values to the final results, and the statistical analysis. We developed a Microsoft® Excel®-based software application coded in Visual Basic for Applications, called Q-Gene, which addresses these points. Q-Gene manages and expedites the planning, performance, and evaluation of quantitative real-time PCR experiments, as well as the mathematical and statistical analysis, storage, and graphical presentation of the data. The Q-Gene software application is a tool to cope with complex quantitative real-time PCR experiments at a high-throughput scale and considerably expedites and rationalizes the experimental setup, data analysis, and data management while ensuring highest reproducibility.
Muller PY, Janovjak H, Miserez AR, Dobbie Z.
Biotechniques 2002 Jun;32(6):1372-1378
Research Group Cardiovascular Genetics, Institute of Biochemistry and Genetics, University of Basel, Switzerland.
In Table 1, the values in the column "Normalized Expression" need to be replaced by the following ones (top to bottom): 2.30E-03, 2.63E-03, 3.92E-03, 2.95E-03, 4.95E-04, 16.79. Additionally, the values in the column "Mean Normalized Expression" need to be replaced by 2.87E-03, 3.26E-04, 11.35. The difference between the two calculation procedures according to Table 2, Equation 2 and 3, respectively, amounts to 2.8%. Furthermore, the corresponding values in the discussion section need to be replaced.
qPCR-DAMS: a Database Tool to Analyze, Manage, and
Store Both Relative and Absolute Quantitative Real-Time PCR data.
Quantitative real-time PCR is an important high throughput method in biomedical sciences. However, existing software has limitations in handling both relative and absolute quantification. We designed qPCR-DAMS (Quantitative PCR Data Analysis and Management System), a database tool based on Access 2003, to deal with such shortcomings by the addition of integrated mathematical procedures. qPCR-DAMA allows a user choose among four methods for data processing within a single software package: (I) Ratio relative quantification, (II) Absolute level, (III) Normalized absolute expression, and (IV) Ratio absolute quantification. qPCR-DAMS also provides a tool for multiple reference gene normalization. qPCR-DAMS has three quality control steps and a data display system to monitor data variation. In summary, qPCR-DAMS is a handy tool for real-time PCR users.
LinRegPCR is a program for the analysis of quantitative RT-PCR (qPCR) data resulting from monitoring the PCR reaction with SYBR green or similar fluorescent dyes. The program determines a baseline fluorescence and does a baseline subtraction. Then a Window-of-Linearity is set and PCR efficiencies per sample are calculated. With the mean PCR efficiency per amplicon, the Ct value per sample and the fluorescence threshold set to determnine the Ct, the starting concentration per sample, expressed in arbitrary fluorescence units, is calculated => See below:
Assumption-free analysis of quantitative real-time PCR data
Ramakers C, Ruijter JM, Deprez RH, Moorman AF. (2003)
Neurosci Lett 2003 Mar 13;339(1): 62-66
Anatomy and Embryology K2-283, Experimental and
Quantification of mRNAs using real-time polymerase chain reaction (PCR) by monitoring the product formation with the fluorescent dye SYBR Green I is being extensively used in neurosciences, developmental biology, and medical diagnostics. Most PCR data analysis procedures assume that the PCR efficiency for the amplicon of interest is constant or even, in the case of the comparative C(t) method, equal to 2. The latter method already leads to a 4-fold error when the PCR efficiencies vary over just a 0.04 range. PCR efficiencies of amplicons are usually calculated from standard curves based on either known RNA inputs or on dilution series of a reference cDNA sample. In this paper we show that the first approach can lead to PCR efficiencies that vary over a 0.2 range, whereas the second approach may be off by 0.26. Therefore, we propose linear regression on the Log(fluorescence) per cycle number data as an assumption-free method to calculate starting concentrations of mRNAs and PCR efficiencies for each sample.
The new LinRegPCR version of the program (with an updated manual) can be downloaded => http://LinRegPCR.nl
Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR dataDespite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as ‘fold-difference’ results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample.
J. M. Ruijter1, C. Ramakers2, W. M. H. Hoogaars1, Y. Karlen3, O. Bakker4, M. J. B. van den Hoff1 and A. F. M. Moorman1
1Heart Failure Research Center, Academic Medical Center, University of Amsterdam, The Netherlands, 2Department of Neuroscience, Faculty of Mental Health, University of Maastricht, The Netherlands, 3Nestec Ltd, PTC Orbe, Switzerland and 4Department of Endocrinology and Metabolism, Academic Medical Center, University of Amsterdam, The Netherlands
Nucleic Acids Research Advance Access published online on February 22, 2009
The new LinRegPCR version of the program (with an updated manual) can be downloaded => http://LinRegPCR.nl
Dear LinRegPCR user,
RDML was developed as a standard for export, exchange, and storage of quantitative PCR data and is supported by several large qPCR system suppliers as well as by data analysis software like qbase-plus. LinRegPCR now forms a link between your qPCR system and such statistical analysis software. LinRegPCR can handle RDML versions 1.0 and 1.1, as well as RDML files in which floating point values are written with decimals points and decimal commas. LinRegPCR will write the analysis results to an RDML file, version 1.1, with decimal points to maintain compatibilty with the current RDML specification.
The RDML input option is the main addition to LinRegPCR that was implemented in 2012. There were also several qPCR systems added to the list of input formats from Excel files. For other minor changes in the program, please have a look at the recent updates listed on the LinRegPCR website (http://LinRegPCR.nl).
On our site you will also find a link to a recent paper (Ruijter et al., Methods 2012), in which LinRegPCR and other publicly available PCR amplification curve analysis programs were compared. This paper is unique in the field of qPCR because all analysis methods were applied by their original developers, and thus in the currently recommended way. The paper was co-authored by the developers of these curve analysis programs and members of the geNorm team, who performed the statistical analysis. The datasets used for this comparison, and the analysis results, can be downloaded from http://qPCRDataMethods.hfrc.nl.
I hope you continue to enjoy the use of LinRegPCR.
Best wishes for the coming festive season and your future scientific endeavours,
Jan M Ruijter
Addressing fluorogenic real-time qPCR inhibition using the novel custom Excel file system 'FocusField2-6GallupqPCRSet-upTool-001' to attain consistently high fidelity qPCR reactions.
Jack M. Gallup and Mark R. Ackermann
Department of Veterinary Pathology, College of Veterinary Medicine, Iowa State University. Ames, Iowa 50011-1250. USA.
Biol. Proced. Online 2006;8:87-152.
The purpose of this manuscript is to discuss fluorogenic real-time quantitative polymerase chain reaction (qPCR) inhibition and to introduce/define a novel Microsoft Excel-based file system which provides a way to detect and avoid inhibition, and enables investigators to consistently design dynamically-sound, truly LOG-linear qPCR reactions very quickly. The qPCR problems this invention solves are universal to all qPCR reactions, and it performs all necessary qPCR set-up calculations in about 52 seconds (using a pentium 4 processor) for up to seven qPCR targets and seventy-two samples at a time – calculations that commonly take capable investigators days to finish. We have named this custom Excel-based file system "FocusField2- 6GallupqPCRSet-upTool-001" (FF2-6-001 qPCR set-up tool), and are in the process of transforming it into professional qPCR set-up software to be made available in 2007. The current prototype is already fully functional.
PREXCEL-Q is not a qPCR data analysis program - it is an extensive qPCR validation, set-up and receipe printout program for each step of the qPCR process; for One-Step, Two-Step and LCM-one or two-step qPCR Test Plate set-ups, avoidance of inhibition by proper dynamic dilution range identificaton and the subsequent final plate set-ups.
Please see attached Dr. Bustin's letter of endorsement of the program to get a feel for what the program really is.
PREXCEL-Q (which is 35 inter-linked Excel files) can only be licensed from Iowa State University by contacting Dr. Dario Valenzuela first at Iowa State University Research Foundation (ISURF) at: email@example.com - and then I personally send the 35 files and password to each new user.
The ‘PREXCEL-Q Method’ for qPCR
Jack M. Gallup, Mark R. Ackermann
Department of Veterinary Pathology, College of Veterinary Medicine, Iowa State University, Ames, Iowa, USA
International journal of Biomedical science 4(4) 2008
The purpose of this manuscript is to describe a reliable approach to quantitative real-time polymerase chain reaction (qPCR ) assay development and project management, which is currently embodied in the Excel 2003-based software program named “PREXCEL-Q” (P-Q) (formerly known as “FocusField2-6Gallup-qPCRS et-upTool-001,” “FF2-6-001 qPCR set-up tool” or “Iowa State University Research Foundation [ISURF] project #03407”). Since its inception from 1997-2007, the program has been well-received and requested around the world and was recently unveiled by its inventor at the 2008 Cambridge Healthtech Institute’s Fourth Annual qPCR Conference in San Diego, CA. P-Q was subsequently mentioned in a review article by Stephen A. Bustin, an acknowledged leader in the qPCR field. Due to its success and growing popularity, and the fact that P-Q introduces a unique/defined approach to qPCR, a concise description of what the program is and what it does has become important. Sample-related inhibitory problems of the qPCR assay, sample concentration limitations, nuclease-treatment, reverse transcription (RT ) and master mix formulations are all addressed by the program, enabling investigators to quickly, consistently and confidently design uninhibited, dynamically-sound, LOG-linear-amplification-capable, high-efficiency-of-amplification reactions for any type of qPCR. The current version of the program can handle an infinite number of samples.
SoFAR: software for fully automatic evaluation of real-time PCR data.
Wilhelm J, Pingoud A, Hahn M.
Justus-Liebig-Universitat Giessen, Giessen, Germany.
Biotechniques. 2003 Feb;34(2):324-32
Quantitative real-time PCR has proven to be an extremely useful technique in life sciences for many applications. Although a lot of attention has been paid to the optimization of the assay conditions, the analysis of the data acquired is often done with software tools that do not make optimum use of the information provided by the data. Particularly, this is the case for high-throughput analysis, which requires a careful characterization and interpretation of the complete data by suitable software. Here we present a software solution for the robust, reliable, accurate, and fast evaluation of real-time PCR data, called SoFAR. The software automatically evaluates the data acquired with the LightCycler system. It applies new algorithms for an adaptive background correction of signal trends, the calculation of the effective signal noise, the automated identification of the exponential phases, the adaptive smoothing of the raw data, and the correction of melting curve data. Finally, it provides information regarding the validity of the results obtained. The SoFAR software minimizes the time required for evaluation and increases the accuracy and reliability of the results. The software is available upon request.
Validation of an algorithm for automatic quantiﬁcation of nucleic acid
copy numbers by real-time polymerase chain reaction
Wilhelm J, Pingoud A, Hahn M.
Anal Biochem. 2003 Jun 15;317(2):218-25.
Institut fur Biochemie, FB 08, Justus-Liebig-Universitat Giessen,
Heinrich-Buff-Ring 58, D-35392 Giessen, Germany.