| Chapter title |
miRNA Expression Profiling: From Reference Genes to Global Mean Normalization
|
|---|---|
| Chapter number | 18 |
| Book title |
Next-Generation MicroRNA Expression Profiling Technology
|
| Published in |
Methods in molecular biology, December 2011
|
| DOI | 10.1007/978-1-61779-427-8_18 |
| Pubmed ID | |
| Book ISBNs |
978-1-61779-426-1, 978-1-61779-427-8
|
| Authors |
D’haene, Barbara, Mestdagh, Pieter, Hellemans, Jan, Vandesompele, Jo, Barbara D’haene, Pieter Mestdagh, Jan Hellemans, Jo Vandesompele |
| 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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| Unknown | 3 | 60% |
Demographic breakdown
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Mendeley readers
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| Brazil | 2 | 1% |
| Germany | 1 | <1% |
| Italy | 1 | <1% |
| Belgium | 1 | <1% |
| Denmark | 1 | <1% |
| Luxembourg | 1 | <1% |
| Unknown | 163 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 42 | 24% |
| Student > Ph. D. Student | 38 | 22% |
| Student > Master | 21 | 12% |
| Student > Bachelor | 15 | 9% |
| Student > Doctoral Student | 14 | 8% |
| Other | 28 | 16% |
| Unknown | 15 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 64 | 37% |
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| Medicine and Dentistry | 28 | 16% |
| Neuroscience | 6 | 3% |
| Immunology and Microbiology | 4 | 2% |
| Other | 14 | 8% |
| Unknown | 21 | 12% |