Abstract
MicroRNAs (miRNAs) are an important class of gene regulators, acting on several aspects of cellular function such as differentiation, cell cycle control, and stemness. These master regulators constitute an invaluable source of biomarkers, and several miRNA signatures correlating with patient diagnosis, prognosis, and response to treatment have been identified. Within this exciting field of research, whole-genome RT-qPCR-based miRNA profiling in combination with a global mean normalization strategy has proven to be the most sensitive and accurate approach for high-throughput miRNA profiling (Mestdagh et al., Genome Biol 10:R64, 2009). In this chapter, we summarize the power of the previously described global mean normalization method in comparison to the multiple reference gene normalization method using the most stably expressed small RNA controls. In addition, we compare the original global mean method to a modified global mean normalization strategy based on the attribution of equal weight to each individual miRNA during normalization. This modified algorithm is implemented in Biogazelle’s qbasePLUS software and is presented here for the first time.
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Acknowledgment
This work was supported by the European Union Framework 7 project SysKid; Grant Number: 241544 (B. D’haene).
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D’haene, B., Mestdagh, P., Hellemans, J., Vandesompele, J. (2012). miRNA Expression Profiling: From Reference Genes to Global Mean Normalization. In: Fan, JB. (eds) Next-Generation MicroRNA Expression Profiling Technology. Methods in Molecular Biology, vol 822. Humana Press. https://doi.org/10.1007/978-1-61779-427-8_18
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DOI: https://doi.org/10.1007/978-1-61779-427-8_18
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