How do I calculate efficiency, and what does it mean? - What value would correspond to perfect efficiency? (Jun/21/2007 )
Here is my standard curve:
(I can't figure out how to make the image come up in this box.)
I calculate the slope of my standard curve to be somewhere around -3.81, but it varies a little from one day to the next.
How do I calculate primer efficiency from this, and what does that efficiency mean in simple terms?
Thanks for any help you can offer -
in short there are 3 hallmarks of an optimized qPCR assay:
1. linear standard curve (r²>0.980)
2. consistency across replicates
and 3. high amplification efficiency (90-105%)
you can calculate the amplification efficiency (E) from the slope of your standard curve using the following formula:
E = 10^(-1/slope)
or E is converted to percentage by E = (10^(-1/slope)-1)*100, in your case (10^(-1/-3.81)-1)*100 = 83%
this is the amount of pcr product increase after each cycle. an ideal reaction reaches an efficiency close to 100%. you should strive for an reaction efficiency of 90-105%. low E can indicate poor primer design, bad reaction conditions, pipetting error,...
try to determine your E over a broad dilution range like 5-6 logs.
Thank you Ned.
What does the r2 value indicate?
Another thing - If I eliminate the points on the left of the plot (which correspond to my most dilute standard, ~160 copies starting amount) my efficiency jumps up to 97%. Now, I know that eliminating data to improve a result is bad science. But I am curious if dilute samples are problematic -- I am wondering if there is a valid reason to think about eliminating the most dilute sample.
Also, our product is 470 bases (we just used the primers we had to hand; didn't design new ones.) Could the length explain the low efficiency?
And finally, even with an efficiency of 83%, since the curve is fairly "pretty" (not much scatter, etc) can it still be used to determine copy number in our experimental samples? Is it still valid to use it? it seems to me that the low efficiency of the primers would be consistent between the standard and the experimental samples .....
first of all i can recommend the following literature http://www.bio-rad.com/pdf/Bulletin_5279B.pdf
i think this is a good information source for a qPCR beginner because it starts from zero and keeps things simple.
concerning your standard curve, i would say that your most dilute standard is outside of the linear dynamic range of your assay. that means results of test samples from that region are not reliable.
maybe you could improve your linear dynamic range by designing primers which are amplifying a shorter amplicon. >400 bp is quite big for qPCR. i would recommend a minimum amplicon lenght of 60-70bp to keep it distinguishable from possible primer dimers.
It's simple, when efficiency is below the suggested range then the reaction efficiency is low. But I've got numbers above (around 111%). What problem could it make (cause)?
Just noting it's a UPL assay (very short - 60 bp and using LNA probe).
efficiency clearly above 100% could be due to inhibitors in the sample which cause a delayed Ct in the samples with highest concentration (they contain also the highest amount of inhibitors) and more diluted samples are less delayed.
Yes, I heard about inhibitors, but same cDNA in other assays don't have such a high efficiencies (I know, it could mean there are inhibitors and lousy efficiency).
And I wouldn't expect those much anyway, because the slope is linear (it wouldn't be if dilution causes noticeable rise) and because I use a purified RNA and RT and qPCR mixes from machine manufacturer, designed to work together.
But I don't have experience in this, I was just thinking if UPL assays don't show generaly better efficiencies for some reason or something.
Well Trof, I don't kn ow exactly about UPL assays, but basically every technique based on the PCR reaction can in theory not have an efficiency above 2, i.e. no more than one copy of every target in each cycle. But the efficiency we look at is based on several calculations which can leed to very distracting results. The general rule is still: efficiency between 1.95 and 2.05 (resp. 95-105%) is okay, everything else is not reliable.
@Patty - the r² is a measure of the goodness of fit of your regression line. If all dots were perfectly lying on the line, the r²-value would be 1.
there are methods to calculate efficiency of PCR assays based on the kinetics of the single reactions. You can avoid the standard curve at all and save money and time.
Take a look at this:
The article explains how the alghoritm works.
Hope you will find it interesting...