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Systematic evaluation of three microRNA profiling platforms: microarray, beads array, and quantitative real-time PCR array.

Wang B, Howel P, Bruheim S, Ju J, Owen LB, Fodstad O, Xi Y - PLoS ONE (2011)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of South Alabama College of Arts and Sciences, Mobile, Alabama, United States of America.

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.

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Related in: MedlinePlus

Signal-to-noise ratio comparison between beads array and LNA array.Plot (a) shows the density curves of the log signal-to-noise ratios for different samples tested by beads array, while the results for LNA array are demonstrated in plot (b). A log signal-to-noise ratio close to “one” indicates that the signal after background subtraction is close to the background noise.
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pone-0017167-g002: Signal-to-noise ratio comparison between beads array and LNA array.Plot (a) shows the density curves of the log signal-to-noise ratios for different samples tested by beads array, while the results for LNA array are demonstrated in plot (b). A log signal-to-noise ratio close to “one” indicates that the signal after background subtraction is close to the background noise.

Mentions: The signal-to-noise ratio (SNR) is a statistical tool that measures the quality of the signals that are obtained from the arrays. When SNR is low, the background noise could dominate the measured expression signal and thus increase the uncertainty in evaluating gene expression levels. We computed the SNR for each miRNA in the LNA and beads arrays by dividing the background-subtracted signal by the estimated background noise. Results show that the beads array has an overall higher SNR than the LNA array (Figure 2). A number of probes have lower intensity than the background on the LNA array, which causes log (SNR) values to be negative. The SNR was not computed for TLDA because there were no estimates for the background noises in qRT-PCR.


Systematic evaluation of three microRNA profiling platforms: microarray, beads array, and quantitative real-time PCR array.

Wang B, Howel P, Bruheim S, Ju J, Owen LB, Fodstad O, Xi Y - PLoS ONE (2011)

Signal-to-noise ratio comparison between beads array and LNA array.Plot (a) shows the density curves of the log signal-to-noise ratios for different samples tested by beads array, while the results for LNA array are demonstrated in plot (b). A log signal-to-noise ratio close to “one” indicates that the signal after background subtraction is close to the background noise.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3037970&req=5

pone-0017167-g002: Signal-to-noise ratio comparison between beads array and LNA array.Plot (a) shows the density curves of the log signal-to-noise ratios for different samples tested by beads array, while the results for LNA array are demonstrated in plot (b). A log signal-to-noise ratio close to “one” indicates that the signal after background subtraction is close to the background noise.
Mentions: The signal-to-noise ratio (SNR) is a statistical tool that measures the quality of the signals that are obtained from the arrays. When SNR is low, the background noise could dominate the measured expression signal and thus increase the uncertainty in evaluating gene expression levels. We computed the SNR for each miRNA in the LNA and beads arrays by dividing the background-subtracted signal by the estimated background noise. Results show that the beads array has an overall higher SNR than the LNA array (Figure 2). A number of probes have lower intensity than the background on the LNA array, which causes log (SNR) values to be negative. The SNR was not computed for TLDA because there were no estimates for the background noises in qRT-PCR.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of South Alabama College of Arts and Sciences, Mobile, Alabama, United States of America.

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.

Show MeSH
Related in: MedlinePlus