How to Normalize qPCR Data? [Webinar] - BioTechniques

Normalization of qPCR data

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Comparison of expression data requires normalization. The optimum normalization method depends on sample type, with the most common being to normalize to reference genes. It is critical to select appropriate reference genes and validate them. However, there are cases when one fails to identify suitable genes for normalization and must use alternatives, and there are cases when normalization of a target gene’s expression to another gene’s expression is most unsuitable. In this webinar, we will learn the goals of normalization, how to select the best normalization strategy and, when normalizing to reference genes, how to select the best reference genes and the optimum number.

In this webinar with Mikael Kubista, you will learn the optimal way to normalize gene expression data; how to identify the best reference genes; the optimum number of reference genes to use for normalization and alternatives to reference gene normalization.

What will you learn?

  • How to identify the best reference genes for normalization
  • How to select the optimum number of reference genes for normalization
  • How to validate reference gene normalization
  • When and how to perform global normalization
  • Alternatives to reference gene normalization and when it should be avoided!

Featured speaker:

Mikael Kubista

TATAA Biocenter

Professor Mikael Kubista is one of the pioneers in molecular diagnostics. He invented the light-up probes, which led to the foundation of LightUp Technologies AB as Europe´s first company focusing on qPCR based diagnostics. In 2001, Kubista set up the TATAA Biocenter as center of excellence in qPCR and gene expression analysis. TATAA biocenter was the first laboratory in Europe to obtain flexible ISO 17025 accreditation and was presented the Frost & Sullivan Award for Customer Value Leadership as Best-in-Class Services for Analyzing Genetic Material in 2013.

Most recently Kubista’s team invented Two-tailed PCR, which is currently the most sensitive and specific method for microRNA analysis. Since 2007, Kubista has headed the Department of Gene Expression at the institute of Biotechnology of the Czech Academy of Sciences.

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