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The results of quantitative PCR analysis we divided into two types: absolute and relative quantitative analysis quantitative analysis.
1, the absolute quantification method, starting with the logarithm of the concentration of the linear relationship between the number of cycles, the standard curve of known copy number of the standard drawing, according to the Ct value of samples, template, sample volume rectifiable.
(1) Preparation of standard:
1V of the sample solution (i) 9V dilution buffer, to be ii;
1V, ii 9V dilution buffer, to be iii;
1V, iii 9V dilution buffer, to be iv;
The iv 9V 1V dilution buffer, to be v.
(2) copies of the calculation: test sample concentration (ng / ul) = OD260 × 40 * dilution
The number of bases × 324 molecular weight sample
Copy number of test sample = concentration of test sample / sample weight × 6 × 1014
Data obtained from the amplification cycle number, a standard curve, we can find sample copies.
(2) the relative quantitative analysis methods:
It can be a double standard curve method and the double-Ct method. The so-called relative, it is only comparing the results before and after the experiment, reflecting the difference between data before and after reaction.
(1) double standard curve method, analysis is simple, but for each gene, each round of experiments to do standard curve, and must have a specific standard for the standard curve, this standard does not represent the true state of amplified samples . This method commonly used in the regulation of gene expression.
F = concentration of the sample target gene / reference gene concentration in the sample ÷ concentration of the control sample of the target gene / reference gene concentrations in control samples
(2) double-Ct method, this method is not the purpose of housekeeping genes and genes as the standard curve, and only need to be treated samples were amplified by PCR testing can be, the difference between the standard curve <0.1. If the amplification efficiency of 100% or between the standard curve and amplification efficiency of each remain the same, more difficult to optimize the experimental conditions such as complex. This analysis method long used in gene expression studies.
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