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Inferring microRNA activities by combining gene expression with microRNA target prediction.

Cheng C, Li LM - PLoS ONE (2008)

Bottom Line: Little computational work has been done to investigate the effective regulation of miRNAs.The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity.The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.

View Article: PubMed Central - PubMed

Affiliation: Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America.

ABSTRACT

Background: MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.

Methodology/principal findings: We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.

Conclusions/significance: A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.

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Related in: MedlinePlus

Distribution of AC scores for 211 known human miRNAs in (A) miR-1 (B) miR-124 (C) 124mut5-6 (D) 124mut9-10 (E) chimiR1/124 and (F) chimiR124/1 transfection data.The AC scores for miR-1 and miR-124 are marked as red circles and blue rectangles, respectively.
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pone-0001989-g001: Distribution of AC scores for 211 known human miRNAs in (A) miR-1 (B) miR-124 (C) 124mut5-6 (D) 124mut9-10 (E) chimiR1/124 and (F) chimiR124/1 transfection data.The AC scores for miR-1 and miR-124 are marked as red circles and blue rectangles, respectively.

Mentions: In Figure 1, we show the distribution of the AC scores for these 211 human miRNAs using box-plots, in which miR-1 and miR-124 are marked as red circles and blue rectangles, respectively. As shown, the regulation of miR-1 (Figure 1A) and miR-124 (Figure 1B) in their transfection microarray profiles are both detected by our method. In the miR-1 transfection data, the AC score of miR-1 is 12.37 at 12 h and 10.37 at 24 h. In the miR-124 transfection data, the AC score of miR-124 is 14.16 at 12 h and 16.11 at 24 h. These four AC scores are the highest among the 211 scores inferred from the corresponding miRNA transfection experiment, with q-values of 0 according to the results from 10,000 permutations.


Inferring microRNA activities by combining gene expression with microRNA target prediction.

Cheng C, Li LM - PLoS ONE (2008)

Distribution of AC scores for 211 known human miRNAs in (A) miR-1 (B) miR-124 (C) 124mut5-6 (D) 124mut9-10 (E) chimiR1/124 and (F) chimiR124/1 transfection data.The AC scores for miR-1 and miR-124 are marked as red circles and blue rectangles, respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2291556&req=5

pone-0001989-g001: Distribution of AC scores for 211 known human miRNAs in (A) miR-1 (B) miR-124 (C) 124mut5-6 (D) 124mut9-10 (E) chimiR1/124 and (F) chimiR124/1 transfection data.The AC scores for miR-1 and miR-124 are marked as red circles and blue rectangles, respectively.
Mentions: In Figure 1, we show the distribution of the AC scores for these 211 human miRNAs using box-plots, in which miR-1 and miR-124 are marked as red circles and blue rectangles, respectively. As shown, the regulation of miR-1 (Figure 1A) and miR-124 (Figure 1B) in their transfection microarray profiles are both detected by our method. In the miR-1 transfection data, the AC score of miR-1 is 12.37 at 12 h and 10.37 at 24 h. In the miR-124 transfection data, the AC score of miR-124 is 14.16 at 12 h and 16.11 at 24 h. These four AC scores are the highest among the 211 scores inferred from the corresponding miRNA transfection experiment, with q-values of 0 according to the results from 10,000 permutations.

Bottom Line: Little computational work has been done to investigate the effective regulation of miRNAs.The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity.The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.

View Article: PubMed Central - PubMed

Affiliation: Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America.

ABSTRACT

Background: MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.

Methodology/principal findings: We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.

Conclusions/significance: A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.

Show MeSH
Related in: MedlinePlus