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Systematic Evaluation of Three microRNA Profiling Platforms: Microarray, Beads Array, and Quantitative Real-Time PCR Array

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  • Bin Wang
  • Paul Howel
  • Skjalg Bruheim
  • Jingfang Ju
  • Laurie B Owen
  • Oystein Fodstad
  • Yaguang Xi

Abstract

Background: A number of gene-profiling methodologies have been applied to microRNA research. The diversity of the platforms and analytical methods makes the comparison and integration of cross-platform microRNA profiling data challenging. In this study, we systematically analyze three representative microRNA profiling platforms: Locked Nucleic Acid (LNA) microarray, beads array, and TaqMan quantitative real-time PCR Low Density Array (TLDA). Methodology/Principal Findings: The microRNA profiles of 40 human osteosarcoma xenograft samples were generated by LNA array, beads array, and TLDA. Results show that each of the three platforms perform similarly regarding intra-platform reproducibility or reproducibility of data within one platform while LNA array and TLDA had the best inter-platform reproducibility or reproducibility of data across platforms. The endogenous controls/probes contained in each platform have been observed for their stability under different treatments/environments; those included in TLDA have the best performance with minimal coefficients of variation. Importantly, we identify that the proper selection of normalization methods is critical for improving the inter-platform reproducibility, which is evidenced by the application of two non-linear normalization methods (loess and quantile) that substantially elevated the sensitivity and specificity of the statistical data assessment. Conclusions: Each platform is relatively stable in terms of its own microRNA profiling intra-reproducibility; however, the inter-platform reproducibility among different platforms is low. More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.

Suggested Citation

  • Bin Wang & Paul Howel & Skjalg Bruheim & Jingfang Ju & Laurie B Owen & Oystein Fodstad & Yaguang Xi, 2011. "Systematic Evaluation of Three microRNA Profiling Platforms: Microarray, Beads Array, and Quantitative Real-Time PCR Array," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-12, February.
  • Handle: RePEc:plo:pone00:0017167
    DOI: 10.1371/journal.pone.0017167
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    References listed on IDEAS

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    1. Rao Youlan & Lee Yoonkyung & Jarjoura David & Ruppert Amy S & Liu Chang-gong & Hsu Jason C & Hagan John P, 2008. "A Comparison of Normalization Techniques for MicroRNA Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-20, July.
    2. Fumiaki Sato & Soken Tsuchiya & Kazuya Terasawa & Gozoh Tsujimoto, 2009. "Intra-Platform Repeatability and Inter-Platform Comparability of MicroRNA Microarray Technology," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-12, May.
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    Cited by:

    1. Swanhild U Meyer & Sebastian Kaiser & Carola Wagner & Christian Thirion & Michael W Pfaffl, 2012. "Profound Effect of Profiling Platform and Normalization Strategy on Detection of Differentially Expressed MicroRNAs – A Comparative Study," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-13, June.
    2. Agnė Šatrauskienė & Rokas Navickas & Aleksandras Laucevičius & Tomas Krilavičius & Rūta Užupytė & Monika Zdanytė & Ligita Ryliškytė & Agnė Jucevičienė & Paul Holvoet, 2021. "Mir-1, miR-122, miR-132, and miR-133 Are Related to Subclinical Aortic Atherosclerosis Associated with Metabolic Syndrome," IJERPH, MDPI, vol. 18(4), pages 1-14, February.
    3. Chuan Wang & Shunyao Yang & Gang Sun & Xuying Tang & Shuihua Lu & Olivier Neyrolles & Qian Gao, 2011. "Comparative miRNA Expression Profiles in Individuals with Latent and Active Tuberculosis," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-11, October.
    4. Bin Wang & Shu-Guang Zhang & Xiao-Feng Wang & Ming Tan & Yaguang Xi, 2012. "Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-12, May.

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