A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Discussion <s> BackgroundPCR has the potential to detect and precisely quantify specific DNA sequences, but it is not yet often used as a fully quantitative method. A number of data collection and processing strategies have been described for the implementation of quantitative PCR. However, they can be experimentally cumbersome, their relative performances have not been evaluated systematically, and they often remain poorly validated statistically and/or experimentally. In this study, we evaluated the performance of known methods, and compared them with newly developed data processing strategies in terms of resolution, precision and robustness.ResultsOur results indicate that simple methods that do not rely on the estimation of the efficiency of the PCR amplification may provide reproducible and sensitive data, but that they do not quantify DNA with precision. Other evaluated methods based on sigmoidal or exponential curve fitting were generally of both poor resolution and precision. A statistical analysis of the parameters that influence efficiency indicated that it depends mostly on the selected amplicon and to a lesser extent on the particular biological sample analyzed. Thus, we devised various strategies based on individual or averaged efficiency values, which were used to assess the regulated expression of several genes in response to a growth factor.ConclusionOverall, qPCR data analysis methods differ significantly in their performance, and this analysis identifies methods that provide DNA quantification estimates of high precision, robustness and reliability. These methods allow reliable estimations of relative expression ratio of two-fold or higher, and our analysis provides an estimation of the number of biological samples that have to be analyzed to achieve a given precision. <s> BIB001 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Discussion <s> Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data. <s> BIB002 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Discussion <s> Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible. <s> BIB003 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Discussion <s> BackgroundMeasuring gene transcription using real-time reverse transcription polymerase chain reaction (RT-qPCR) technology is a mainstay of molecular biology. Technologies now exist to measure the abundance of many transcripts in parallel. The selection of the optimal reference gene for the normalisation of this data is a recurring problem, and several algorithms have been developed in order to solve it. So far nothing in R exists to unite these methods, together with other functions to read in and normalise the data using the chosen reference gene(s).ResultsWe have developed two R/Bioconductor packages, ReadqPCR and NormqPCR, intended for a user with some experience with high-throughput data analysis using R, who wishes to use R to analyse RT-qPCR data. We illustrate their potential use in a workflow analysing a generic RT-qPCR experiment, and apply this to a real dataset. Packages are available from http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.htmland http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.htmlConclusionsThese packages increase the repetoire of RT-qPCR analysis tools available to the R user and allow them to (amongst other things) read their data into R, hold it in an ExpressionSet compatible R object, choose appropriate reference genes, normalise the data and look for differential expression between samples. <s> BIB004 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Discussion <s> 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 RT-qPCR 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 cycle-axis 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 co-author on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification 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 (http://qPCRDataMethods.hfrc.nl). <s> BIB005 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Discussion <s> Two surveys of over 1,700 publications whose authors use quantitative real-time PCR (qPCR) reveal a lack of transparent and comprehensive reporting of essential technical information. Reporting standards are significantly improved in publications that cite the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, although such publications are still vastly outnumbered by those that do not. <s> BIB006
In our review, we provide a comprehensive survey of software packages and tools for the analysis of qPCR data, including Cq calculation, efficiency determination, normalization, and quantification. In addition, we report tools dedicated for the analysis of dPCR experiments. The information we provide represents a valuable resource for researchers working with qPCR data and supports users with the selection of the appropriate analysis tool for a specific application. Surveyed software packages and tools are manifold and vary in the supported methods and usability. The choice of the correct method to analyze the qPCR data at hand strongly depends on the experimental setup and assay. Therefore, a number of review articles were recently published BIB005 BIB001 to facilitate the choice of the most suitable analysis method for a particular application. Windows tools for quantification analysis include standalone applications and tools based on Microsoft Excel. "LRE analyzer" is a recently developed, platform independent tool with an intuitive interface that can be used for absolute quantification without the need for construction of target-specific standard curves. REST is a popular tool available on the Microsoft Windows platform, which has been released in four versions so far. Amongst the reviewed tools, three applications are built on top of Microsoft Excel, where qBase has been cited the most. CopyCaller is the only tool dedicated to the analysis of CNVs. Web-based tools include CAmpER, Cy0 Method, MAKERGAUL, PCR-Miner, and QPCR. QPCR offers the most comprehensive functionality and supports, in addition to storing and sharing of raw and processed data, relative quantification of qPCR data. MAKERGAUL and QPCR can also be installed on a private server where configuration and management may be challenging. PCR-Miner is a widely used tool for calculating Cq and efficiency values. The user interfaces of CAmpER, Cy0 Method, and PCR-Miner do not comply with current standards and thus the tools may be difficult to use. Several R-packages are available based on the statistical computing and graphics environment R, which usually do not offer a graphical user interface and need to be used on command line. They provide an excellent choice for users who wish to keep the pipeline for the analysis transparent and highly customizable. The currently available packages enable the seamless integration of analysis strategies for experimental qPCR platforms and commercial platforms. Furthermore, it has been demonstrated in several occasions that custom-made cross-platform GUIs and report generators can be created with little programming effort. In order to Table 2 Quantification software packages and tools for qPCR. Listed are software packages and tools capable of performing quantification analysis. The Cq column was ticked if the tool is able to perform Cq calculation from raw fluorescence data. If the tool is able to calculate amplification efficiencies (based on dilution series or on raw fluorescence data) the appropriate column was ticked. Tools are marked for absolute quantification (Abs quant) if they are able to output absolute quantification values. In order to qualify for relative quantification (Rel quant) fold change values after relative quantification need to be returned. Error propagation denotes that ability of the tool to propagate the error throughout the various analysis steps. The normalization column was ticked if the tool implements one or several normalization techniques (not limited to normalization to reference genes). NA handling describes the possibility to deal with missing values after initial Cq calculation. Graphs were checked if the tools included methods for graphical (quantification) result presentation. Statistics was ticked, if the tool includes methods for statistical analysis or direct calls to underlying statistical frameworks after obtaining quantification or normalization results. facilitate data input, the package ReadqPCR has been developed to parse raw qPCR data from different platforms BIB004 . Moreover, most cyclers are able to export raw data in various forms, which can be imported by custom-made parsers via the powerful import and export facilities of R. Since all described R packages inherit the properties of the R computing environment, we would like to emphasize that parallelization of analysis steps and distribution as service (e.g., shiny) are easy to implement. Among the surveyed R-packages that support normalization and quantification, the script based qpcR library currently provides the most comprehensive functionality ranging from different methods for Cq and efficiency calculation to quantification and data visualization. HTqPCR provides different normalization techniques that can be used when reference genes are not present or not reliably expressed. dPCR is an R-package for the analysis of dPCR data providing complete functionality to analyze the respective experiment. In addition to R-based packages, tools for other platforms using MATLAB, Perl, Python, and SAS have been published where the Python based tool pyQPCR offers the most extensive functionality. The tool offers a state-of-the art user interface and is based on the highly cited methods from BIB002 . A recent survey of publications mentioning qPCR revealed that methods used for analyzing the data were poorly reported BIB006 . Consequently, the MIQE guidelines specify that in addition to the analysis results detailed information about the applied methods need to be published. Furthermore, to allow repeatable and reproducible research the computer code used to analyze the data has to be made available to others BIB003 . Amongst others, the MIQE guidelines list essential information that has to be submitted with the manuscript: qPCR analysis program, Cq method, normalization methods, and statistical methods for results significance. Moreover, the exact software version and source code of newly developed methods need to be published as well. The use of specific qPCR analysis software allows providing detailed information on the methods of data analysis and confidence estimation. We therefore encourage using published, peer-reviewed software that proved to be suitable for qPCR data analysis. In addition, the MIQE guidelines provide specific recommendations, which need be considered when performing qPCR data analysis. Emphasis is placed on the importance of PCR efficiency, especially when reporting mRNA concentrations for target genes relative to those of reference genes. Researchers need to either ensure that the genes were amplified with comparable efficiencies or include the specific amplification efficiencies in the analysis. Furthermore, when data is normalized against reference genes at least two reference genes have to be included in the analysis, unless data is presented that shows that a single reference gene is invariantly expressed under the experimental conditions described. Table 2 includes information about tools that adhere to these MIQE recommendations. Over the years, different qPCR platforms and many analysis tools have been made available, usually supporting their individual file format. As a consequence, the input file for each analysis tool needs to be created either manually or semi-automatically. Only a very limited number of tools support the direct use of export files from various qPCR systems. The introduction of the RDML file format aimed to encourage the exchange of qPCR data between instrument software, data analysis tools, and researchers. Unfortunately, RDML is not widely spread amongst the surveyed tools and software packages -so far only three applications are able to work with RDML data. In order to allow a more streamlined and comparable analysis of qPCR data, more vendors and tools need to support the standardized data exchange format RDML.