Four tips for RT-qPCR data normalization using reference genes
October 23, 2013
The use of multiple stable reference genes is generally accepted as the method of choice for RT-qPCR data normalization (Vandesompele et al., Genome Biology, 2002; Bustin et al., Clinical Chemistry, 2009). Biogazelle’s qbase+ software greatly facilitates the process of validating reference genes and performing state-of-the-art normalization using the geometric mean of multiple validated reference genes.
A measured difference in RNA expression level between two samples is the result of both true biological as well as experimentally induced (technical) variation. Different variables, inherent to the RT-qPCR workflow need to be controlled for in order to minimize the technical variation. Influencing parameters include the amount and quality of starting material, enzymatic efficiencies, and overall transcriptional activity.
It is highly recommended to minimize the technical variation by using standard operating procedures throughout the entire qPCR workflow. The remaining technical variation should then be further reduced or removed by using a proper normalization approach, enabling a better appreciation of the true biological variation.
The use of multiple stable reference genes is generally accepted as the method of choice for RT-qPCR data normalization (Vandesompele et al., Genome Biology, 2002; Bustin et al., Clinical Chemistry, 2009). Biogazelle’s qbase+ software greatly facilitates the process of validating reference genes and performing state-of-the-art normalization using the geometric mean of multiple validated reference genes.
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