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 identiﬁ ed. Within this exciting ﬁ eld of research, whole-genome RT-qPCRbased miRNA proﬁ ling in combination with a global mean normalization strategy has proven to be the most sensitive and accurate approach for high-throughput miRNA proﬁ ling (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.