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We test the TaqMan® Gene Expression assay by running a no-template control (NTC) with each assay. We guarantee that assays run in a NTC reaction will not produce detectable amplification signal (Ct > 38). If you are seeing amplification with your assay in NTC reactions, you may have one of these problems that is explained in our Real-Time PCR Troubleshooting Tool.

There are many possible causes for no amplification from a sample, such as inhibitors or natural expression levels. Please see the full details on this in our Real-Time PCR Troubleshooting Tool.

The efficiency of the PCR should be between 90% and 100% (−3.6 ≥ slope ≥ −3.3). If the efficiency is 100%, the Ct values of the 10-fold dilution will be 3.3 cycles apart (there is a 2-fold change for each change in Ct). If the slope is below –3.6, then the PCR has poor efficiency. For causes of poor efficiency please see more in our Real-Time PCR Troubleshooting Tool.

Amplification curves with a sigmoidal, or S, shape are often caused by either an incorrect baseline or too much background fluorescence. Please see our Real-Time PCR Troubleshooting Tool for more details on how to correct this, along with visuals.

There are several factors that can contribute to delayed amplification. Please see our Real-Time PCR Troubleshooting Tool for more details on what to look for and how to address this problem.

If your amplification curve looks like the example below, then you are seeing what we call the ‘waterfall’ effect. Look at the amplification plot for the affected wells in a linear view, and check to see where the baseline should be set. The baseline should be set such that the end cycle Ct is 1–2 cycle units before the amplification starts. Image A shows a well with a waterfall effect, which is corrected in image B by setting a manual baseline.

Perform a literature search in PubMed with your sample/target gene, for publications performing microarray or qPCR. Often you can see what other researchers use as their endogenous controls and compile a list of potential endogenous controls. You can also screen for potential endogenous controls by ordering human/mouse/rat endogenous control array plates. These plates are preplated with 32 endogenous control genes in triplicates on a 96-well plate.

You can perform an absolute quantification experiment to quantitate unknowns based on a known quantity using a standard curve.

Software Data Analysis

You can use DataAssist™ software to analyze data in which the endogenous control is not on every plate.  Simply export the study results .txt file from the instrument software and open this file in DataAssist™.

You can use DataAssist™ (or ExpressionSuite) software to generate p-values from ddCt data.  You must first assign biological groups to your samples, and there needs to be at least 2 samples in each group.  

You can analyze your data in DataAssist™ (or ExpressionSuite) software using either a multiple endogenous control option or global normalization.  Global normalization can be used when a large number of targets are being studied.

The analysis using multiple endogenous controls is based on this paper: 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 Biology 3, research0034 (2002).

The analysis for global normalization is based on this paper: Mestdagh P.,Van Vlierberghe P., De Weer A., Muth D., Westermann F., Spelemean F., Vandesompele J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biology 10,R64 (2009).

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