Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study

  • A Corrigendum to this article was published on 28 August 2014


MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription–quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: MiRQC study overview.
Figure 2: Titration response and reproducibility.
Figure 3: Platform accuracy.
Figure 4: Detection rate, sensitivity and specificity.
Figure 5: Differential miRNA expression.
Figure 6: Radial plot of performance metric z-scores.

Accession codes

Primary accessions


Gene Expression Omnibus

Change history

  • 30 July 2014

    In the version of this article initially published, the author Linda Wong was omitted from the author list. The error has been corrected in the HTML and PDF versions of the article.


  1. 1

    Cortez, M.A. et al. MicroRNAs in body fluids–the mix of hormones and biomarkers. Nat. Rev. Clin. Oncol. 8, 467–477 (2011).

    CAS  Article  Google Scholar 

  2. 2

    Jensen, S.G. et al. Evaluation of two commercial global miRNA expression profiling platforms for detection of less abundant miRNAs. BMC Genomics 12, 435 (2011).

    CAS  Article  Google Scholar 

  3. 3

    Wang, B. et al. Systematic evaluation of three microRNA profiling platforms: microarray, beads array, and quantitative real-time PCR array. PLoS One 6, e17167 (2011).

    CAS  Article  Google Scholar 

  4. 4

    Kelly, A.D. et al. Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: preliminary assessment of cross-platform concordance. Genomics 102, 8–14 (2013).

    CAS  Article  Google Scholar 

  5. 5

    Kolbert, C.P. et al. Multi-platform analysis of microRNA expression measurements in RNA from fresh frozen and FFPE tissues. PLoS One 8, e52517 (2013).

    CAS  Article  Google Scholar 

  6. 6

    Leshkowitz, D., Horn-Saban, S., Parmet, Y. & Feldmesser, E. Differences in microRNA detection levels are technology and sequence dependent. RNA 19, 527–538 (2013).

    CAS  Article  Google Scholar 

  7. 7

    Pradervand, S. et al. Concordance among digital gene expression, microarrays, and qPCR when measuring differential expression of microRNAs. Biotechniques 48, 219–222 (2010).

    CAS  Article  Google Scholar 

  8. 8

    Git, A. et al. Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 16, 991–1006 (2010).

    CAS  Article  Google Scholar 

  9. 9

    Meyer, S.U., Kaiser, S., Wagner, C., Thirion, C. & Pfaffl, M.W. Profound effect of profiling platform and normalization strategy on detection of differentially expressed microRNAs–a comparative study. PLoS ONE 7, e38946 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Shippy, R. et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat. Biotechnol. 24, 1123–1131 (2006).

    CAS  Article  Google Scholar 

  11. 11

    Dittmar, K.A., Goodenbour, J.M. & Pan, T. Tissue-specific differences in human transfer RNA expression. PLoS Genet. 2, e221 (2006).

    Article  Google Scholar 

  12. 12

    Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

    Article  Google Scholar 

  13. 13

    Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  Google Scholar 

  14. 14

    Geiss, G.K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26, 317–325 (2008).

    CAS  Article  Google Scholar 

  15. 15

    Lefever, S. et al. RDML: structured language and reporting guidelines for real-time quantitative PCR data. Nucleic Acids Res. 37, 2065–2069 (2009).

    CAS  Article  Google Scholar 

  16. 16

    Mestdagh, P. et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 10, R64 (2009).

    Article  Google Scholar 

  17. 17

    Breitling, R., Armengaud, P., Amtmann, A. & Herzyk, P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83–92 (2004).

    CAS  Article  Google Scholar 

Download references


We thank J. Nuytens for preparing all RNA samples and E. Lefebvre and T. Demoor for statistical and mathematical support. P. Mestdagh is supported by the Fund for Scientific Research Flanders (FWO); J.V. is supported by the Ghent University Research Fund (BOF).

Author information




P. Mestdagh, J.V. and T.G. designed the study; P. Mestdagh and J.V. performed data analysis and wrote the manuscript; and N.H., L.B., D.A., N.B., C.C., D.C., P.D., M.D., L.D., S.D., Y.F., S.F.-S., B.G., J.G., C.G., E.L., K.Y.L., S.L., P. Mouritzen, A.N., S. Patel, S. Peiffer, S.R., G.S., D.S., J.M.S., E.J.S., S.S., U.U., V.V., F.S. and T.P. produced miRNA expression data. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Jo Vandesompele.

Ethics declarations

Competing interests

D.A. and P. Mouritzen are employed by Exiqon; N.B., C.C., K.L., S.P atel., E.S. and U.U. are employed by Life Technologies; D.C., Y.F. and D.S. are employed by Quanta Biosciences; P.D. and S.F. are employed by Agilent Technologies; M.D. and J.G. are employed by Affymetrix; L.D. and C.G. are employed by NanoString Technologies; S.D. and S.S. are employed by WaferGen; E.L. and J.S. are employed by Qiagen; S.L. and G.S. are employed by Illumina. MiRNA profiling experiments were performed at the expense of Exiqon, Life Technologies, Agilent Technologies, Quanta Biosciences, Qiagen, WaferGen, Illumina and NanoString Technologies.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10, Supplementary Tables 1–3 and Supplementary Notes 1–13 (PDF 15764 kb)

Supplementary Data

miRQC qPCR Data (ZIP 920 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mestdagh, P., Hartmann, N., Baeriswyl, L. et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods 11, 809–815 (2014). https://doi.org/10.1038/nmeth.3014

Download citation

Further reading