File Exchange

image thumbnail

PCRefficiency

version 2.0.0.0 (15 KB) by Giuseppe Cardillo
Set the Efficiency of a RT-PCR to use in the relative quantification of transcripts.

1 Download

Updated 04 Apr 2018

GitHub view license on GitHub

Reverse transcription(RT) followed by PCR is a powerful tool for the detection and quantification of mRNA. It is the most sensitive method for the detection and quantification of gene expression levels, in particular for low abundance mRNA. The relative quantification is based on the expression ratio of a target gene versus a reference gene. Some mathematical models have already been developed to calculate the relative expression ratios, with or without efficiency correction. Normally the PCR efficiency is set at 2 (the max possible value) for the reference and target gene, but a difference in PCR efficiency of 0.03 between the target and reference gene, the falsely calculated difference in expression ratio is 46% in case of Et<Er and 209% in the case of Et>Er. The difference will increase dramatically by higher efficiency differences: i.e. DE=0.05 (27% and 338%) and DE=0.1 (7.2% and 1083%) This function computes the efficiency of PCR reaction and is based on MYREGR function. If it is not present on the computer, pcreff will try to download it from FEX
Created by Giuseppe Cardillo
giuseppe.cardillo-edta@poste.it

To cite this file, this would be an appropriate format: Cardillo G. (2008) PCREfficiency: set the Efficiency of a RT-PCR to use in the relative quantification of transcripts. http://www.mathworks.com/matlabcentral/fileexchange/20887

Comments and Ratings (0)

Updates

2.0.0.0

inputparser; table implementation; github link

1.5.0.0

Switched from natural logarithm to decimal logarithm to uniform the output to Roche and Applied Biosystems outputs

1.4.0.0

This routine uses MYREGR function. If it is not present on the computer, PCREff will try to download it from FEX.

1.3.0.0

Changes in description

1.2.0.0

Changes ih help section

MATLAB Release Compatibility
Created with R2014b
Compatible with any release
Platform Compatibility
Windows macOS Linux