First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments

Transcription

1 RealTime ready Application Note June 2011 First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments Heiko Walch and Irene Labaere Roche Applied Science, Penzberg, Germany Abstract/Introduction This technical application note describes the first steps in data analysis for relative quantification of gene expression experiments performed using RealTime ready Custom Panels. The easy initial setup as well as the Result Export procedure of the LightCycler 480 Software is described. Further preparation of the data depends on the means of analysis. Three routes to analyze the data are described: 1. Spreadsheet calculation software 2. GenEx Software from MultiD Analysis 3. qbase PLUS 2.0 from Biogazelle Whereas GenEx and qbase PLUS are software packages from third party vendors specialized in RT-qPCR data analysis (1, 2) the spreadsheet calculation approach can be seen as a generic procedure for performing subsequent analysis either in the spreadsheet software or in other sophisticated statistical tools such as R, SigmaPlot, etc. (3, 4) Note that this article does not focus on qpcr setup, experimental planning or correct selection of reference genes. For details on this important topics, see publications such as the MIQE guidelines (5), or other recently published scientific best practices for qpcr expression studies (6, 7, 8). For life science research only. Not for use in diagnostic procedures. RealTime ready

2 Setting Up the Experiment and Selecting the Assays A thoroughly planned experiment is vital to good scientific data. The RealTime ready Configurator (9) offers various possibilities to facilitate the creation of a reasonable list of target genes. For example, the focus lists available under the Search by Focus Panel function comprise RealTime ready assay collections for different fields of interest, such as: Genes involved in various signaling pathways (e.g., NF-kB, Jun, MAPK, sonic hedgehog, Notch, wnt) The gene list was generated using the focus list of assays for genes Amplified/overexpressed genes in cancers, based on a publication from Santarius et al. (10). Additionally, some genes from the NF-kB pathway were added via the Search by Pathway function in the RealTime ready Configurator. RealTime ready Custom Panel Genes involved in specific biological processes (e.g., angiogenesis, oncology, apoptosis, induction of pluripotency, stem cell differentiation) Genes belonging to certain families or groupings (e.g., protein kinases, phosphatases, proteases, transcription factors) These lists can be seen as scientifically sound suggestions, and the RealTime ready Configurator also offers the freedom to add and remove assays to the desired layout as needed. To demonstrate the first analytical steps in a multi-plate analysis, we set up an artificial time series experiment with two biological samples (S01 and S02) and two time points (T01 and T02) after treatment measured in duplicates for the cdna synthesis step and a single qpcr measurement each. With a total of 93 genes of interest and 3 reference genes, we chose a 384-well plate layout with 4 replicates on each plate (see Figure 1). Replicates: 4 Assays: 93 Reference Genes: user selected user selected user selected Figure 1: Sample layout for a 384-well plate with 4 replicates of 93 targets of interest and 3 reference genes each. Performing the RealTime ready Experiment on the LightCycler 480 Instrument RealTime ready Custom Panels can be customized on the RealTime ready Configurator (9). The selected assays are pre-plated, and ready to use on LightCycler 480 Multiwell Plates. Single assays are also available in liquid format. The My Orders page of the Configurator shows the current status of all orders. After an order is produced and shipped General : Pos General : Target Name Rel Quant : Combined Sample/Target Type A1 HS PRKCI Target Unknown A2 HS AKT Target Unknown N5 HS NFKB Target Unknown N6 HS ALAS Ref Unknown N7 HS TRAF Target Unknown the page contains a.zip file that can be downloaded and is associated with the configuration. The archive contains two text files: 1. The sample editor import file with layout information (see Figure 2) 2. The configuration info file with additional assay information (see Figure 3) Figure 2: An excerpt of the sample editor import file. The three columns contain information for the well coordinates (General:Pos), the contained assays (General:Target Name), and the type of the assay (Rel Quant: Combined Sample/Target Type). The assay information is represented as a concatenation of the organism, the gene symbol, and the assay ID, all separated by vertical dashes ( ). The different types of assays are either a gene of interest (Target Unknown), a reference gene (Ref Unknown), or an assay from the control concept (Unassigned Unknown) not used for this demonstration. The file can be imported directly into the LightCycler 480 sample editor software or opened and modified in any text editor (for example, to change the reference genes or gene annotations) and then reimported afterwards. 2 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments

3 Figure 3: Configuration info file containing assay annotation and sequence information. The example configuration info file shown above contains additional information regarding the plate layout, gene information, and assay details including the primer sequences. Combined with the detailed assay information and annotations available at the RealTime ready Configurator, all information required by the MIQE guidelines for scientific publications is provided (5). The contents of the archive greatly reduce the time needed to annotate and set up the experiments in the LightCycler 480 Software. After creating a new experiment in the LightCycler 480 Software (11), the provided file can be imported into the sample editor to assign all well and assay information for this configuration (see Figure 4). The information on how the replicates on the plate are used is plate-specific and also needs to be edited in the sample editor. It is essential to use consistent naming for samples, replicates, and any additional information that will be needed later to combine the data. Figure 4: Editing target and sample information using the sample editor import file in the sample editor of the LightCycler 480 Software. Preparing and Exporting the Result Data from the LightCycler 480 Software 1.5 The LightCycler 480 Software offers several analysis modules. For this example we want to evaluate the relative expression levels of mrnas in different biological samples. To do this we compare the normalized C q values which can be used as a relative measurement for the abundance of cdna covered by the particular RealTime ready assay. The C q values are generated using the Abs. Quant Analysis and the automated second derivative method (11). Finally to do a meaningful comparison of different samples and genes the C q values need to be annotated with sample and gene information accordingly as demonstrated below. RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 3

4 Exporting the Result Data from the LightCycler 480 Software In order to combine the results of multiple plates with varying numbers of replicates, use absolute quantification in conjunction with the second derivative method for C q -calling. The actual relative quantification is done outside the LightCycler Software, following standard RT-qPCR analysis procedures (11). Looking at the analysis results, some well positions might be highlighted in green, indicating that no C q value could be calculated. Such failures may occur for many reasons and can be expected if large amounts of different assays are used with uncharacterized samples. RealTime ready assays are function tested with a standard cdna sample, therefore missing values can be interpreted as no expression of the relevant target or, to be more precise, an expression beyond the level of detection for that particular assay. For data analysis purposes, those missing values can be dealt with in various ways, some of which will be explained briefly in the analysis sections. If, however, there are any clear patterns visible, for example, one single plate replicate or one column/row shows no calls you should check the starting materials and the pipetting schemas for possible technical errors. Figure 5: Export of the result C q values in the LightCycler 480 Software by right-clicking in the result table and saving the file to an appropriate location. After the Abs. Quant. Analysis, the result data needs to be exported by right-clicking in the result table in the analysis section of the LightCycler 480 Software for every individual run (see Figure 5). For the sake of data quality, consider how and where to organize the data. In this context, having a dedicated folder structure, with for example, file names and/ or short experimental descriptions along with the result and IXO files names for a particular round of experiments, is a good start (see Figure 6). For this workflow example, the experiment consisted of 3 LightCycler 480 Instrument runs saved as result_config_id_platexyz.txt. Figure 6: File and folder structure for Experiment. 1. Short experiment description; 2. Sample editor import file; 3. Zip file provided by Roche containing the sample editor import file and assay annotation/information; 4 & 5. Exported result files for the individual plates. After exporting the results, the run and data gathering step is finished and the data needs to be cleaned and combined in order to be analyzed. 4 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments

5 General Considerations for Relative Quantification Expression Data Multi-plate multi-parameter analysis can be conducted in several ways; this note covers only the first steps of cleaning and combining the results. These steps are usually constant and independent of the level of sophistication followed in the statistical analysis afterwards. For a fold change analysis these steps are: 1. Plausibility check and quality control of the results 2. Normalization versus sample-specific calibrators or references 3. Combining the replicates 4. Calculation of relative ratios of the expression levels between different samples For comparing relative expression ratios, we will focus on the C q method (12). This simplification of the Pfaffl method (13) assumes that the qpcr efficiencies are more or less equal and ~100%. The RealTime ready assays are pre-tested and fulfill strict quality criteria (14). One of the requirements is that the assay efficiency is in the range of % (2.0 +/- 10%). Most assays are located in a very narrow efficiency window from (see Figure 7). Therefore, a first analysis can be done without efficiency correction and using the C q method. If, however, one needs to qualify very small expression changes with good statistical power, or if a sample preparation is known to have issues regarding qpcr efficiency, carefully estimate the assay efficiencies in the particular experimental workflow and use the results for efficiency correction in the analysis workflow as described by Pfaffl (13). Figure 7: Distribution of the PCR efficiencies of more than 6,000 RealTime ready qpcr assays. The mean efficiency of the sample is 1.99 with a standard deviation Ninety percent of the efficiencies are located within a window from 1.95 to As a second simplification, we will not do any inter-plate correction with a universal calibrator sample, although with RealTime ready plate layouts containing replicates the concept is optional. The technical plate-to-plate variance using RealTime ready assays in conjunction with the recommended LightCycler 480 Probes Master, and the LightCycler 480 System is negligible. Therefore, an additional inter-plate calibration step is not explained in detail, and may actually be skipped for many qpcr expression analyses using the LightCycler 480 System. Basic Excel Analysis There are numerous spreadsheet calculation software packages available for different platforms (e.g. Excel/ Microsoft Office, Numbers/iWorks or Calc/OpenOffice). Here Excel which is part of the Microsoft Office package and available for most prominent operating systems is used for demonstrational purposes. Most of the methods introduced below can either be found identically or they can be substituted by similar functions in the other software packages. Although it can be used for basic to advanced data analyses, it has some drawbacks and is not as powerful or easy to handle as the specialized qpcr data analysis software packages also mentioned in this note. The descriptions and screenshots shown here were created using a Office 2010 version of Excel on a PC running the Windows 7 operating system. 1. Importing the LightCycler 480 Data File into Excel All data values in the previously exported result files are separated by tabulators. This tab delimited format is easy to edit, and most software is able to handle the data correctly with minor adjustments. In Excel, data can be imported using the text import wizard or by simply copy/pasting from a standard text editor. In this example, the files were added underneath each other in one spreadsheet. For the plate_02 file, only the data rows were imported and the header lines were deleted. During concatenation of the result sets, it is useful to add an additional column holding the plate information (see Figure 8). RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 5

6 Basic Excel Analysis continued 2. Combining qpcr Data with Gene Names and Target Type Information The table now contains the C q values and well positions, but neither the gene names nor the target type information. Both of these pieces of information are needed and are part of the sample editor import file provided on the Configurator. Consequently, the next step is to combine the C q values with the associated gene names. One quick way to do this is by using the Excel vertical lookup function. The VLOOKUP ( SVERWEIS in German versions) is an invaluable tool for combining different tables having one common identifier for each row. In the case of the result file and the sample editor import file, the common denominator is the well position. The positions are fixed and strictly associated with the assay for one particular configuration. The VLOOKUP function takes the well coordinate from the result table and tries to find it in the lookup table (here the sample editor sheet). An additional parameter to the function specifies how many columns the function should look to the right to pass the value of a neighboring cell (for details, see Figure 9, as well as the associated Help in the application). Figure 8: Imported LightCycler 480 Software result files. The grayed-out columns are not required for later analysis and can be deleted or hidden. During the course of concatenating the files, column I was created and the plate information was added manually. Figure 9: Using VLOOKUP function in Excel for combining data from different tables. Here the General:Target Name is part of the SampleEditorImportFile sheet and needs to be added to appropriate lines of the LC_ _result sheet. The common identifier or key between the two tables is the well coordinate (A1 to P24) present in both tables. The blue highlighted Excel cell in the LC_ _results sheet contains the coordinate value I1. In the SampleEditorImportFile sheet, you can manually scroll down until you find the same well coordinate I1 in line 194. The second column in the same sheet holds the data for the gene target name HS YWHAB that can now be manually pasted into the LC_ _result sheet again. This is done automatically by the VLOO- KUP statement framed in blue: =VLOOKUP (C3,SampleEditorImportFile!A:B,2,0). Look up the value from cell C3 in the SampleEditorImportFile sheet in column A. If found, return the value of the second (2) column to the right of it. If no perfect match is found, no value is returned (0). This only needs to be entered once and can be copied to the cells underneath either by dragging the cell or copying the content, marking the rest of the column, and pasting the content. Afterwards, every row in the LC_ _result sheet contains the matching target information. The target type can be added accordingly. 6 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments

7 3. Normalization against Sample-specific Reference Genes After this combination step, the data is ready to be analyzed, leading to the second step of the C q procedure, which is applying the appropriate sample specific references for normalization. Normalization against sample-specific reference genes is important to compensate for the inevitable intrinsic experimental variations that could otherwise lead to biases in the results. A variance in input RNA or different cdna synthesis efficiencies are two examples of these confounding experimental variances. The selection of good reference genes is crucial but often neglected, as several publications have shown over the last several years. Without going into too much detail, reference gene selection usually requires a pilot experiment with a set of candidate reference genes, in which the samples are treated in the same way as planned for the final experiment. As a plausible general rule Figure 10: Pivot tables are a versatile tool for creating new pivoting views on data tables. for the various algorithms, (15, 16, 17) used to evaluate the best reference gene set, a good reference gene s expression should be unaffected by any of the experimental procedures to be assessed. In concordance with recent publications, we recommend using more than one reference gene (3 are used as a default for nearly all the RealTime ready layouts). Besides literature searches, there are tools to narrow down the list of candidate reference genes for such pilot studies (e.g. 18). The RealTime ready Reference Gene Focus Panel is a reasonable and pragmatic selection, having the benefit that all included genes are also directly available as designated reference genes in the RealTime ready Configurator. If the available references are not sufficient, any gene or assay can be used as reference due to the layout flexibility of the target genes in the Configurator. In our example, we have chosen 3 reference genes: GUSB (GeneID: 2990), HRPT1 (GeneID: 3278), and ALAS1 (GeneID: 211), and we will use the mean C q of the references as a normalization factor. Naturally, the normalization to the reference genes has to be performed for every replicate separately. This can be archived multiple ways in Excel. For demonstration purposes, we have chosen a method that additionally exemplifies another powerful and versatile Excel tool the pivot tables. Looking at our now enriched result table, one recognizes that each individual sample or replicate is characterized by the plate ID and the sample name (see Figure 9). As a first intermediate step, we add another column ( Tag ) containing the concatenated values of the name and plate column. Second, we insert a pivot table using the newly created tag as line identifier, calculating the mean of the C q values shown and filtering to show only Ref Unknown target types (see Figure 10). In each value line, the pivot table now contains one value that represents the mean C q value for the three reference genes for one replicate/sample from one plate. These values are now subtracted from the matching (sample and plate) raw C q values in the LC_ _result table, either manually or using a few mouse clicks in the VLOOKUP function (see Figure 11). Figure 11: Using VLOOKUP to normalize versus the mean of the references for each replicate on each plate. RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 7

8 Basic Excel Analysis continued The Normalized by reference column now contains the deviation of the gene of interest s expression compared to the mean reference gene s expression. Now a direct comparison of individual values from different samples and different plates is possible, and basic analyses can begin. Keep in mind that the data shows C q value differences, and assuming 100% PCR efficiencies, a value of 1 converts to a 2-fold difference (e.g., FoldDiff =2 Cq_target Cq_meanRef ). As an additional QC step, it is reasonable to assess how the different biological replicates on the plates performed. This can be done graphically with a scatterplot, as shown in Figure 12. Depending on the type of planned analysis, it may be useful to arrange the gene names as columns and the sample/ replicates as individual rows of the result table. In a two-step procedure, the current result table can be transformed into this kind of data view. First, another pivot table is created (see Figure 13). The data is then copied and pasted using the Transpose option into a new data sheet. The result is shown in Figure 14. Figure 12: Scatterplot of a biological replicate comparison (using S01_T01_plate_01 values on the X-axis and S01_T01_plate_02 on the Y-axis) shows a very good R 2 correlation coefficient of 0.98 and exemplifies the reproducibility of biological replicates on different plates in this experiment. Figure 13: A pivoted view of the data using the target name as row identifier and the Normalized by reference values for each individual sample. The replicates of each sample over different plates are aggregated by using the mean value of the calculated differences. Figure 14: In the transposed data view, the individual samples can be plotted against each other more intuitively. These are only the very first steps of data analysis using Excel. The used functionalities will most likely have to be applied several times in varying orders to get a good understanding of the experimental results. Excel can be used for these analyses, and doing it semi-manually is initially very informative. However, many of the steps during qpcr analysis must be done repeatedly, and there are other software tools specifically created for analyzing qpcr data. In the following section, we introduce two powerful software suites that are easy to use with RealTime ready assay data. 8 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments

9 First Analysis Steps Using the GenEx Software Suite GenEx (developed by MultiD) is a software suite tailored around the analysis of qpcr data. The software is available in three different versions, each providing basic to highly advanced statistical analysis capabilities. For additional information, visit Recently, MultiD Analysis developed a special import wizard for RealTime ready plates that is now available as a separate plug-in module for the GenEx Software package. The module greatly streamlines the analysis workflow and sets a new standard for ease-of-use and analysis of multi-plate/multi-parameter qpcr experiments. Detailed descriptions and tutorials describing the usage of the import wizard as well as the capabilities and usage of the software are available at the MultiD website (1). The wizard module is self-explanatory and takes the generated and exported result files (result_config_id_platexyz.txt), as well as the provided LightCycler 480 Software sample editor import file for creating the assay-to-well position association (see Figure 15). Figure 15: The RealTime ready import wizard is started from the tools section of the GenEx Software package, and offers an extremely rapid and convenient way to start analyzing multiple-plate qpcr experiments. Figure 16: The LightCycler 480 result export files and the sample editor import file must be specified during the wizard workflow. After specifying the panel type (Focus or Custom Panel), the format (96 or 384), and the layout of the particular configuration (in this case, 384 wells with 4 replicates and 96 genes each), the previously exported data files can be uploaded. In addition, to add the gene of interest and reference gene information, the sample editor import file needs to be selected (see Figure 16). The reference genes specified in the configuration are automatically taken from the annotated sample editor import file together with all target and sample names. Identical sample names can be used later to identify replicates and may be used downstream for QC and data analysis. After finishing the wizard, the result data from the three plates is saved as a GenEx-specific.mdf file, and can be edited and analyzed using the extensive capabilities of the software package. RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 9

10 First Analysis Steps Using qbase PLUS qbase PLUS is a flexible and powerful qpcr analysis software developed and maintained by Biogazelle (2). The original release from 2007 was replaced in 2011 by qbase PLUS 2.0, which is offered in two license versions with extending feature sets. The premium license is available as time limited trial version and is required for analyzing the data from the 384-well plate experiments. Biogazelle also offers an extensive set of tutorials to get started with the software package at their website (for details see the Biogazelle website). qbase PLUS can already handle a predefined LightCycler 480 result export format. This format can be easily generated with the standard result exports in combination with the sample editor import file containing the target names for every well coordinate. The first step is again combining the result export and the sample editor import as previously shown in Figure 8. Starting with this table generated in the Excel analysis section we create a data table as shown in Figure 17. qbase PLUS has preconfigured layouts for different qpcr machines including the Roche LightCycler Instrument series. The software expects the data to be in the correct format with the data columns in the correct order as described in Figure 17. Sample files and importing descriptions are available at the Biogazelle website (2). Figure 17: Data format required by qbase PLUS for LightCycler 480 results. The columns A, C, E, F, G and H with dark blue headers are required for correct data import. In order to use the sample and target information later in qbase PLUS the software offers the option to overload the Name column with the sample information and the target information concatenated by one of various separators. Here we have chosen to use, as delimiter and combined columns J, D and K by using the special character & in a simple Excel function as seen in the cell E2 above. The added plate information from column J creates unique sample names in this experiment. The function can be dragged or copied to the remaining cells of column E. After completion column D needs to be hidden before exporting the data as described below. Figure 18: The Run Import dialog and the qbase PLUS main window with the Project Explorer. Right-clicking onto individual items of the navigational tree offers context-dependent options. Right-clicking on Experiments allows the addition of a new experiment to the project, and right-clicking on Runs leads to the Import Run dialog shown here. For the RealTime ready result files as generated in the course of this note, it is important to set the different parameters as shown here (TAB delimited and LightCycler 480 file type). After preparing the Excel file one needs to re-export the results for each plate which can be easily done by hiding column D filtering for the plate_01 in column J and copy and pasting the remaining table to a new text file named qbase_results_lc_ _plate01.txt (for example). The filtering and exporting is repeated for all plates in the experiment. Having prepared the result files one can start importing the results in qbase PLUS. Start by creating a new project by right-clicking in the Project Explorer pane in the main qbase PLUS window as shown in Figure 18. The context specific right-click is an intuitive user interface basis of qbase PLUS. Create a new experiment to import the Run data again by right-clicking on Experiments. After expanding the experiments navigation tree, right click on Runs allow the user to import the previously prepared qpcr result (see Figure 18). 10 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments

11 The samples are identified and all genes are imported as Targets of interest. The next step is to set the reference genes for further calculations. In the case of the RealTime ready panels where the references were identified in previous experiments this can be archived by opening the Targets of interest branch, selecting the genes to be set as references and setting the target type to Reference Target. Alternatively, qbase PLUS offers to identify suitable references within the set of genes by using the genorm algorithm (15), which can be accessed in the Analysis section of the Project Explorer pane. An interesting feature in this context is that qbase PLUS presents the genorm results graphically as well as in an interpreted textual format. Further statistical analysis can be started from the analysis section (see Figure 19) starting from Multi-target bar-charts to sophisticated statistical tests. Figure 19: Basic analysis and the Stat. Wizard in qbase PLUS. The Stat. wizard option helps the user to choose the right statistical test for the data (t-test, Mann-Whitney, paired or unpaired etc.). This is helpful although choosing the appropriate test for the data should be already clear during the experimental planning. Another feature worth mentioning is the option to annotate the assays with additional information (for example the ordering number or the primer sequences as provided with RealTime ready assays). Summary Experimental planning and data analysis are vital parts of sound scientific work including expression analysis by RT qpcr. One of the crucial and most time-consuming first steps is designing and validating good qpcr assays for the targets of interest. This burden is removed by using RealTime ready assays which are all carefully designed and function tested in our lab before being released to the RealTime ready Configurator (9, 14). The second obstacle in the path is designing an experiment that is suited to shine new light on the addressed biological question. Choosing the assays and targets in an appropriate layout is again facilitated using the search capabilities and the gene annotation in the Configurator. Although good lab work must not be underestimated, getting the data (and hence C q values) is often trivial and routine work. The last big hurdle is usually data analysis and getting the relevant results from seemingly complex data. This note demonstrates some basic concepts and first steps in different software solutions. Although it is feasible to do the analysis in spreadsheet software like Excel, it is advisable to carefully consider the time and money spent on doing these analyses. Using one of the dedicated software packages is often more straightforward and many of the pitfalls that await the entry-level statistician are addressed and taken care of. These software suits can directly deal with all MIQE compliant information supplied with the RealTime ready assays, greatly simplifying the workflow. RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 11

12 Bibliography 1. MultiD GenEx: 2. Biogazelle qbase PLUS 2.0: 3. R Development Core Team: R A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna Austria Sysstat Software Inc. SigmaPlot: 5. Bustin Stephen A.: The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem Apr;55(4): Derveaux S., Vandesompele J., Hellemans J.: How to do successful gene expression analysis using real-time PCR. Methods Apr;50(4): Kubista M. & Sindelka R.: The prime technique Real time PCR analyis G.I.T. Laboratory Journal 9 10, pp Kitchen RR, Kubista M, Tichopad A. Statistical aspects of quantitative real-time PCR experiment design. Methods Apr;50(4): Roche Applied Science The RealTime ready Configurator: Santarius T, Shipley J, Brewer D, Stratton MR, Cooper CS. A census of amplified and overexpressed human cancer genes. Nat Rev Cancer Jan;10(1): Roche Applied Science LightCycler Real-Time PCR Systems Application Manual: index.jsp?&id=htc_ Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods Dec;25(4): Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res May 1;29(9):e Roche Applied Science RealTime ready Whitepaper: realtimeready/index.jsp?&id=rt_ Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol Jun 18;3(7) 16. Jensen J., Ørntoft T.: Normalization of real-time quantitative RT-PCR data: a model based variance estimation approach to identify genes suited for normalization applied to bladder- and colon-cancer data-sets.cancer Research (64): Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-Excel-based tool using pair-wise correlations. Biotechnol Lett Mar;26 (6): Hruz T, Wyss M, Docquier M, Pfaffl MW, Masanetz S, Borghi L, Verbrugghe P, Kalaydjieva L, Bleuler S, Laule O, Descombes P, Gruissem W, Zimmermann P. RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization. BMC Genomics Mar 21;12:156 For life science research only. Not for use in diagnostic procedures. LIGHTCYCLER and REALTIME READY are trademarks of Roche. All other product names and trademarks are the property of their respective owners. License Disclaimer information is subject to change or amendment. For current information on license disclaimers for a particular product, please refer to the Online Technical Support page ( Published by Roche Diagnostics GmbH Sandhofer Straße Mannheim Germany 2011 Roche Diagnostics. All rights reserved. 0611