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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

Expression distributions of miRNAs being profiled by the three platforms.Plots (a), (c) and (e) demonstrate the density curves of the expression of all miRNAs being profiled by LNA array, beads array, and TLDA, respectively. Plots (b), (d) and (f) compare the density curves of the expressions of the overlapped miRNAs from all three platforms. We took a log2 transformation to all intensity measures. For TLDA data, the  values were used.
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pone-0017167-g001: Expression distributions of miRNAs being profiled by the three platforms.Plots (a), (c) and (e) demonstrate the density curves of the expression of all miRNAs being profiled by LNA array, beads array, and TLDA, respectively. Plots (b), (d) and (f) compare the density curves of the expressions of the overlapped miRNAs from all three platforms. We took a log2 transformation to all intensity measures. For TLDA data, the values were used.

Mentions: Figure 1 compares the distributions of the log2 intensity measures for all the samples tested by the three platforms. The left panel illustrates the distribution of all miRNAs from three different platforms while the right panel displays the distribution of the 213 shared miRNAs. For each plot, we see that within each platform, the distributions of different profiles (without normalization) show similar patterns. By comparing each pair of plots in each row, especially (a) versus (b) and (e) versus (f), we find that the left modes are lower after the non-overlapped miRNAs are excluded. Meanwhile, the patterns of the distributions maintain similarity, indicating that a majority of the non-overlapped miRNAs are weakly expressed.


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)

Expression distributions of miRNAs being profiled by the three platforms.Plots (a), (c) and (e) demonstrate the density curves of the expression of all miRNAs being profiled by LNA array, beads array, and TLDA, respectively. Plots (b), (d) and (f) compare the density curves of the expressions of the overlapped miRNAs from all three platforms. We took a log2 transformation to all intensity measures. For TLDA data, the  values were used.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0017167-g001: Expression distributions of miRNAs being profiled by the three platforms.Plots (a), (c) and (e) demonstrate the density curves of the expression of all miRNAs being profiled by LNA array, beads array, and TLDA, respectively. Plots (b), (d) and (f) compare the density curves of the expressions of the overlapped miRNAs from all three platforms. We took a log2 transformation to all intensity measures. For TLDA data, the values were used.
Mentions: Figure 1 compares the distributions of the log2 intensity measures for all the samples tested by the three platforms. The left panel illustrates the distribution of all miRNAs from three different platforms while the right panel displays the distribution of the 213 shared miRNAs. For each plot, we see that within each platform, the distributions of different profiles (without normalization) show similar patterns. By comparing each pair of plots in each row, especially (a) versus (b) and (e) versus (f), we find that the left modes are lower after the non-overlapped miRNAs are excluded. Meanwhile, the patterns of the distributions maintain similarity, indicating that a majority of the non-overlapped miRNAs are weakly expressed.

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