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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 the pre-scores for miR-124 in 10,000 permutated data sets.
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pone-0001989-g006: Distribution of the pre-scores for miR-124 in 10,000 permutated data sets.

Mentions: Since the pre-scores of different miRNAs may have different distributions under the hypothesis, they are not directly comparable. Therefore, we normalize the pre-score into the AC score defined as the following:(4)where MEAN(psperm) is the mean of psperm and SD(/psperm/) is the standard deviation of the absolute values of psperm. In General, the permutated pre-score has a bimodal distribution as shown in Figure 6. It can be shown that if the expression change profile e is symmetric with respect to zero, the permutated pre-score would also have a symmetric distribution. In most microarray data, the symmetric assumption for the expression change profile e is approximately satisfied and therefore we use SD(/psperm/) to combine the standard deviations of the permutated pre-scores in the positive and negative sides. Certainly, the symmetry is not perfect: the distribution of the pre-scores may skew to one side. We use MEAN(psperm) to correct the skewness, which is usually close to zero. In practice, the AC score achieves a good normalization for the pre-scores of different miRNAs. The AC score can be either positive or negative: a positive value indicates an overall down-regulation of the target genes of a miRNA and thereby the enhanced activity of the miRNA. Conversely, a negative value indicates an overall up-regulation of the target genes of a miRNA and thereby the reduced activity of the miRNA.


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

Cheng C, Li LM - PLoS ONE (2008)

Distribution of the pre-scores for miR-124 in 10,000 permutated data sets.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0001989-g006: Distribution of the pre-scores for miR-124 in 10,000 permutated data sets.
Mentions: Since the pre-scores of different miRNAs may have different distributions under the hypothesis, they are not directly comparable. Therefore, we normalize the pre-score into the AC score defined as the following:(4)where MEAN(psperm) is the mean of psperm and SD(/psperm/) is the standard deviation of the absolute values of psperm. In General, the permutated pre-score has a bimodal distribution as shown in Figure 6. It can be shown that if the expression change profile e is symmetric with respect to zero, the permutated pre-score would also have a symmetric distribution. In most microarray data, the symmetric assumption for the expression change profile e is approximately satisfied and therefore we use SD(/psperm/) to combine the standard deviations of the permutated pre-scores in the positive and negative sides. Certainly, the symmetry is not perfect: the distribution of the pre-scores may skew to one side. We use MEAN(psperm) to correct the skewness, which is usually close to zero. In practice, the AC score achieves a good normalization for the pre-scores of different miRNAs. The AC score can be either positive or negative: a positive value indicates an overall down-regulation of the target genes of a miRNA and thereby the enhanced activity of the miRNA. Conversely, a negative value indicates an overall up-regulation of the target genes of a miRNA and thereby the reduced activity of the miRNA.

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