Sheng Zhao* and Russell D. Fernald
Department of
Biological Sciences & Program in Neuroscience
Stanford,
News: Miner 2.1 has been
developed! The current website has also been updated to the new version which
is at least ten times faster, more accurate and more stable. It also support
much larger sample data (like 384-well plate).
For Miner user, is it possible to purchase the license and get an executable software package of Miner for research groups?
For Real-time PCR instrument manufactures and bio-software company, is it possible to purchase the license/patent and integrate Miner into their software package for various Real-time PCR machines?
Yes, it is!
Please feel free to contact our Licensing Associate at Stanford, Imelda Oropeza. who is helping us with the license issue. Her address is:
Imelda Oropeza, Licensing Associate,
Stanford University
Office of Technology Licensing
E-Mail:
imelda@stanford.edu
Phone: 650-725-9039, Fax: 650-725-7295
(Patent / License reference number (pending): S04-261 at Stanford University Office of Technology Licensing)
How to cite Miner? Sheng Zhao, Russell D. Fernald. Comprehensive algorithm for
quantitative real-time polymerase chain reaction. J. Comput. Biol. 2005
Oct;12(8):1045-62. PubMed and PDF ABSTRACT Quantitative real-time
polymerase chain reactions (qRT-PCR) have become the method
of choice for rapid, sensitive, quantitative comparison of RNA transcript
abundance. Useful data from this method depends on fitting data to theoretical
curves that enable computation of mRNA levels. Calculating qRT-PCR results
requires parameters such as reaction efficiency and the fractional cycle
number at threshold (CT) to be used however many algorithms currently in
use estimate these important parameters. Here we
describe an objective method for quantifying qRT-PCR results using calculations
based on the kinetics of individual PCR reactions
without the need of the standard curve, independent of any assumptions or
subjective judgments which allow direct calculation of efficiency and CT. We use a four-parameter logistic
model to fit the raw fluorescence data as a function of PCR cycles to
identify the exponential phase of the reaction. Next, we use a three-parameter simple
exponent model to fit the exponential phase using an iterative non-linear
regression algorithm. Within the exponential portion of the curve, our
technique automatically identifies candidate regression values using the P-value of regression and then
uses a weighted average to compute a final efficiency for quantification. For
CT determination, we chose the first positive second derivative
maximum from the logistic model. This algorithm provides an objective
and noise-resistant method for quantification of qRT-PCR results that is
independent of the specific equipment used to perform PCR reactions. Keywords: Quantitative polymerase
chain reaction; Four-parameter logistic model; Three-parameter simple
exponent model; Noise-resistant algorithm; Platform independent * Correspondence to: Sheng Zhao
Lance Kriegsfeld Lab Phone: 1-510-643-9899 Email: windupzs@gmail.com
Neurobiology Research Laboratory
Department of Psychology and
Helen Wills Neuroscience Institute
3210 Tolman Hall
UC Berkeley
Berkeley, CA 94720