I would like to know if there is a Standard number of cycles for Virues and Bacterial PCR single and multiple targets

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    $\begingroup$ No, there isn't. It's completely dependent on the sample, how it was prepared, the quality of the template, and the viral or bacterial load in the sample. Try something, if there's no signal run more cycles, if the signal is overwhelming run less. That's science. $\endgroup$ – MattDMo Apr 14 '16 at 22:44
  • $\begingroup$ Or, run qPCR and study the amplification curves... $\endgroup$ – MattDMo Apr 14 '16 at 22:45

No, there isn't.

Quantitative PCR (qPCR) uses a specific (e.g. TaqMan) or non-specific (e.g. SYBR Green) dye to detect the point at which the quantity of dsDNA (or a specific template) exceeds a threshold during thermocycling. Assuming you have an appropriate primer, the quantity of target doubles (at most) during each thermal cycle, so the cycle at which the signal exceeds the threshold (the cycle threshold, or Ct value) gives you information about the amount of target initially present.

However, the amount of accessible target in a sample depends on many factors including sample processing, extraction efficiency, sample storage, or the presence of RNases/DNases. The PCR efficiency during cycling also varies depending on conditions. There are also many other sources of variation in qPCR results, for example the platform you're using, the risk of contamination, the cycling program.

In theory, you can keep running a qPCR until you get a signal, but in practice you would stop after about 40 cycles at most unless you were specifically designing an assay to detect extremely low copy numbers since after that the most likely explanation for a positive result is sample contamination. 40 cycles should be enough to detect a template that was only present at 1/1,000,000,000,000 of your detection threshold to begin with. When designing qPCR experiments purely for detection (as opposed to quantification) it is best to include a negative control which helps rule out sample contamination etc. I recommend reading the answer to this previous question to help understand why we stop after a certain number of samples, and see this paper as an example of the risks of not using an appropriate control.

There is some guidance on designing good qPCR experiments at http://www.rdml.org/miqe.php along with a checklist for best practice (although this is specifically aimed at quantitative qPCR studies so not everything on their checklist will be relevant if you are simply thinking about presence/absence).


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