Baselines in Real-Time PCR -- Ask TaqMan®: Ep. 5

Examine a standard real-time PCR amplification plot showing geometric, linear, and plateau phases. Recognize the noise or 'junk' that appears in early cycles before the actual amplification signal emerges.

Learn that noise must be minimized by establishing a baseline—a cycle range containing only noise before amplification appears. The software subtracts this noise on a well-by-well basis to improve data quality.

Switch to linear scale and compare data before and after baselining. Show how unbaselied data causes samples to start at different y-axis points, while baselining normalizes all samples to start at zero.

Explain that Delta Rn is the background-normalized value plotted on the y-axis of log-scale amplification curves. Note the relationship to ROX for further reference.

Establish baseline range manually under analysis settings. Choose to apply the same subtraction to all wells of an assay or set baselines individually for each well under advanced settings.

Select the default auto-baselining setting, which calculates and subtracts noise individually for each well. This method generally produces optimal results without manual intervention.

Identify when auto-baselining fails by observing abnormally s-shaped curves, indicating the end cycle was set too low. Switch to manual mode and raise the N cycle value until curves return to normal shape.