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State-of-the-art normalization of RT-qPCR data
Setting up a qPCR experiment is so simple that it actually becomes dangerous. Without appropriate controls and data normalization, results can be misleading at best.
During this webinar, Dr Vandesompele addresses selection and validation of suitable reference genes as well as the use of the global mean normalization method to obtain accurate data. He also describes tools for data generation and analysis.
Covered in this video:
- Critical elements contributing to the success of qPCR results
- Why do we need normalization?
- Various normalisation strategies
- The problem with using a single non-validated reference gene
- The genNorm solution to the normalisation problem
- geNorm expression stability parameter
- geNorm algorithm
- Calculation of the normalization factor
- geNorm validation
- Normalization using multiple stable reference genes
- Large and active geNorm discussion community
- geNorm pilot experiment
- Intermezzo - RNA quality has an impact on expression stability
- A new Normalization method: global mean normalization
- How to validate a new normalization method
- geNorm ranking
- Reduction of experimental variation
- Better identification of differentially expressed miRs
- Normalisation strategies in qbasePLUS