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1. Can I analyze data from my qPCR instrument in qBasePlus?

In principle, qBasePlus can read data export files from any real-time PCR instrument software, as long as the user organizes the data into a format currently known by qBasePlus. To this purpose, 2 general and instrument independent formats are available (simple and qBase). In addition, qBasePlus directly reads export files (containing Ct values, not the raw (fluorescent) data or binary files) from the following instruments:

Guidelines and example files can be found in the data import section.

2. What are the hardware requirements for running qBasePlus?

We recommend at least 1024 Mb RAM memory and a Pentium IV, Athlon or equivalent processor. In fact, hardware requirements are strongly dependent on the number of PCR wells to be analyzed and on the actual experimental setup. Larger experiments may require 2048 Mb of RAM and a new generation processor (e.g. with multiple cores). We succesfully analyzed fifty 384-well plates (equivalent of 200 96-well plates) on a 2048 Mb RAM laptop with a Core Duo T2400 1.83 GHz processor.

3. How do I install qBasePlus?

In Windows, you need administrator installation rights and the most recent version of Java. qBasePlus installation starts automatically after double cliking on the downloaded installer file.

For Linux, there is no installer. Download and extract qBasePlus to an appropriate folder (e.g. /usr/local/qbaseplus). Set the execution bit for qBasePlus ('chmod 001 qbaseplus' on the command line, or file properties in the graphical user environment).  qBasePlus requires SUN Java to operate; you can get it using your package manager, or from http://www.java.com/en/download/linux_manual.jsp.

4. Where can I find my install ID?

In qBasePlus, go to Help > Licensing > Create license. Please note that the install ID is computer specific; a license key generated using this install ID will only work on the computer that generated the install ID.

5. Where is the user manual?

There is no traditional written manual. We have tutorial videos that document most important aspects of the operational workflow of real-time PCR data analysis using qBasePlus. However, do not hesitate to contact us if you need support (only for users with a full license).

6.How to activate and install a purchased license key?

You need to log in to your private mybiogazelle space, and navigate using the left menu to 'my licenses' and 'license overview'. Activate your license key by clicking on it and providing your install ID (see FAQ 'Where can I find my install ID'). After activation, you can download your license key (*.lic file) and unlock qBasePlus by going to Help > Licensing > Install license, and browsing to the location on your hard disk or other medium where you saved the license key.

7. Why do I have to activate my license key?

All qBasePlus license keys are specific for a given computer. During activation, computer specific information is embedded in the encrypted license key. This is done by integrating your install ID (see FAQ 'Where can I find my install ID') that is generated by qBasePlus after installation on your computer. The license key can therefore only be installed on the computer used for generation of the install ID.

8. How do I migrate from qBase to qBasePlus?

Using the qBase Run Extraction tool, you can export all your former qBase experiments into individual and annotated qBase run files. All files belonging to the same experiment can be imported at once in qBasePlus using the batch import mode (just select multiple files for import) and selecting 'qBase' as import file format. Please note that batch import of data files is only available in the fully licensed version of qBasePlus.

9. What is inter-run calibration and how does it work?

Inter-run calibration is a calculation procedure to detect and remove (often underestimated) inter-run variation. Detailed information is available in the qBase paper (Hellemans et al., Genome Biology, 2007). The basic principle is that the experimenter measures one or (preferentially) more identical samples in different (to be calibrated) runs (in conjunction of course with many other samples that actually are different across the runs). These identical samples (so-called inter-run calibrators) can then be used to detect and correct inter-run variation.

qBasePlus is the only software that allows inter-run calibration using more than one inter-run calibrator (making it more accurate), that performs inter-run calibration after normalization (allowing the experimenter to re-synthesize cDNA from the inter-run calibrator RNA samples), and that propagates the error introduced during the inter-run calibration procedure. A tutorial is available that demonstrates how to calibrate runs in qBasePlus.

Please note that in contrast to the phased out qBase, inter-run calibrator samples should have a different name in the different run when using qBasePlus.

10. How does qBasePlus deal with replicate measurements?

qBasePlus automatically deals with technical replicates or repeated measurements. These are recognized as different PCR wells with an identical sample and target name. The Cq values of all (non-empty) wells of a replicate group are averaged at the very beginning of the calculation workflow. Of course, outliers can be removed before calculations (see tutorial on quality control).

With it comes to biological replicates, the current version of qBasePlus can handle this kind of replicates i in terms of relative quantification, normalization, error propagation and inter-run calibration, as long as you give a different sample name to each biological replicate. The downstream biostatistical analysis is not yet incorporated; for now, you need to export the result table and use your favorite statistical program to process the data (calculating means, confidence intervals, and apply a statistical test and do power analysis). Biostatistical analysis of biological replicates will be built into a future version of qBasePlus.

A third type of replicate measurements are the so called inter-run calibrators. These are identical samples that are measured for the same target in different runs (in order to detect and correct inter-run variation, see also Frequently Asked Question nr. 7). To avoid interpretation of inter-run calibrators as technical replicates, they should have a different sample name in the different runs (e.g. IRC1_a, IRC1_b, ...).  Users should then indicate that a number of sample names actually refer to the same biological sample that is used as an inter-run calibrator (e.g. both IRC2_a and IRC2_b refer to the second inter-run calibrator, sample IRC2). This procedure is known as setting the inter-run calibrators (more info in the inter-run calibration tutorial).

11. How to flag bad technical replicates based on a standard deviation threshold?

qBasePlus flags bad replicates based on a user defined maximum allowed difference in Cq values (defined in the Experiment quality control settings). Of note, there is a clear relation between the standard deviation and difference in Cq (independent of the actual Cq values). In fact, the standard deviation increases 0.1 units per 0.14142 cycle difference between duplicated reactions; similarly, the standard deviation increases 0.07071 units per 0.1 cycle difference between duplicated reactions. Hence, if you want to flag bad duplicates that differ by more than 0.2 standard deviations, you need to use a 0.28284 cycle difference. For triplicates, a standard deviation threshold of 0.1 means that the highest and lowest Cq value can only differ by maximum 0.1 Cq values from the middle point.

12. How to get support?

If you need specific information, or run into a problem, first check the tutoral videos that illustrate most functionality in qBasePlus. If this does not help, please go over the Frequently Asked Support Questions (see higher). If all fails, please use the dedicated Support form that is available from your personal 'my support' page after logging in to the Biogazelle website (mybiogazelle section top right). Provide as much information as possible to facilitate trouble shooting. This includes a description of what you were trying to achieve and what you were doing before the problem occurred.  If an error occurred, qBasePlus will store relevant information in a log file. The content of this log file is available in the Error Log view (Window > Show View > Error Log) by clicking the Export Log icon. When reporting an error, please upload this file.

13. How is PCR efficiency calculated and used for the relative quantification?

The PCR efficiency E can be calculated from the slope of a serial dilution as follows: E = 10^(-1/slope) - 1 (with an E of 1 being perfect, indicating 100% efficiency). The formula to go from Cq values to relative quantities is (E+1)^delta-Cq (hence 2^delta-Cq for an assay with 100% PCR efficiency).

The gold standard method for PCR efficiency estimation is a serial dilution of representative template (e.g. a mixture of RNA/cDNA from your samples). Nevertheless, there are a few algorithms (from the large number out there) that seem to provide a reliable estimate of the PCR efficiency based on a single amplification curve. Importantly, the caclulated results should be precise and accurate (and many algorithms fail in this respect). Hence, various papers (see references below) point at the danger of using sample specific PCR efficiencies based on a single amplification curve (or even replicate measurements). The authors rather propose to average the sample specific efficiencies to a target (gene) specific efficiency. This is also wat we recommend to our qBasePlus users.

In qBasePlus, users should provide the base of the exponential function as amplification efficiency value for relative quantification if they want to correct for target specific efficiency. The base number is the E value + 1, e.g. 1.95 for 95% efficiency (E value of 0.95).

References: Nordgård et al., Anal Biochem, 2006; Goll et al., BMC Bioinformatics, 2006; Karlen et al., BMC Bioinformatics, 2007.

14. What signifies a CNRQ value in the result table?

CNRQ means Calibrated Normalized Relative Quantity (see Hellemans et al., Genome Biology, 2007 - http://genomebiology.com/2007/8/2/R19). If one does not perform inter-run calibration, then CNRQ equals NRQ and is the PCR efficiency corrected target of interest relative quantity (RQ) divided by the geometric mean of the corrected reference target RQs. If one uses only one reference target (not recommended), then the target of interest RQ is divided by the reference target RQ.