Cited by 607

Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression

Git, Anna, Dvinge, Heidi, Salmon-Divon, Mali, Osborne, Michelle, Kutter, Claudia, Hadfield, James, Bertone, Paul, Caldas, Carlos·2010·RNA

Abstract

RNA abundance and DNA copy number are routinely measured in high-throughput using microarray and next-generation sequencing (NGS) technologies, and the attributes of different platforms have been extensively analyzed. Recently, the application of both microarrays and NGS has expanded to include microRNAs (miRNAs), but the relative performance of these methods has not been rigorously characterized. We analyzed three biological samples across six miRNA microarray platforms and compared their hybridization performance. We examined the utility of these platforms, as well as NGS, for the detection of differentially expressed miRNAs. We then validated the results for 89 miRNAs by real-time RT-PCR and challenged the use of this assay as a “gold standard.” Finally, we implemented a novel method to evaluate false-positive and false-negative rates for all methods in the absence of a reference method.

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  • miRNA profiling results obtained using different platforms can vary dramatically for individual miRNAs despite good overall inter-platform correlation.

    “In addition to the pre-analytical potential challenges, researchers from many laboratories have now found that miRNA profiling results obtained using different platforms can vary dramatically for individual miRNAs despite good overall inter-platform correlation [17–21].”

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Research works printed but not linked: 6

  1. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, et al. 2005. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33: e179. doi: 10.1093/nar/gni178. · DOI
  2. Chen J, Lozach J, Garcia EW, Barnes B, Luo S, Mikoulitch I, Zhou L, Schroth G, Fan JB. 2008. Highly sensitive and specific microRNA expression profiling using BeadArray technology. Nucleic Acids Res 36: e87. doi: 10.1093/nar/gkn387. · DOI
  3. Hennessy E, O'Driscoll L. 2008. Molecular medicine of microRNAs: Structure, function, and implications for diabetes. Expert Rev Mol Med 10: e24. doi: 10.1017/S1462399408000781. · DOI
  4. Sato F, Tsuchiya S, Terasawa K, Tsujimoto G. 2009. Intra-platform repeatability and inter-platform comparability of microRNA microarray technology. PLoS One 4: e5540. doi: 10.1371/journal. pone.0005540. · DOI
  5. Sheng Y, Engstrom PG, Lenhard B. 2007. Mammalian microRNA prediction through a support vector machine model of sequence and structure. PLoS One 2: e946. doi: 10.1371/journal.pone. 0000946. · DOI
  6. Warren P, Taylor D, Martini PGV, Jackson J, Bienkowska J. 2007. PANPÀa new method of gene detection on oligonucleotide expression arrays. In 7th IEEE International Conference on Bio- informatics and Bioengineering, 2007, pp. 108-115, Boston, MA.

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Authors
Git, A., Dvinge, H., Salmon-Divon, M., Osborne, M., Kutter, C., Hadfield, J., Bertone, P., & Caldas, C.
Venue
RNA
Published
2010-04-01
Volume
16
Issue
5
Pages
991-1006
ISSN
  • 1355-8382
  • 1469-9001
Cited by
607
References
3
Language
en

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