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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

Average AC scores and p-values for miR-1 in the miR-1 transfection profile at 12 h (A and B) and 24 h (C and D) based on perturbed miR-1 target prediction scores.The x-axis shows the perturbing percentage from 0% to 50%. At each perturbing percentage, 100 perturbed binding score profiles of miR-1 are produced by exchanging the binding scores of target and non-target genes of miR-1. The standard deviations of AC scores and p-values are also shown.
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pone-0001989-g003: Average AC scores and p-values for miR-1 in the miR-1 transfection profile at 12 h (A and B) and 24 h (C and D) based on perturbed miR-1 target prediction scores.The x-axis shows the perturbing percentage from 0% to 50%. At each perturbing percentage, 100 perturbed binding score profiles of miR-1 are produced by exchanging the binding scores of target and non-target genes of miR-1. The standard deviations of AC scores and p-values are also shown.

Mentions: It is known that in silico miRNA target prediction is usually not accurate. Depending on the cut-off setting, the false positive rate and/or the false negative rate of the target predictions could be fairly high. Nonetheless, our method achieves accurate inference of miRNA activity modification in the miRNA transfection data as shown above. To investigate the robustness of our method to the false miRNA target predictions, we introduce additional errors to the miRNA target prediction data and examine whether our method is still able to identify the activity enhancement of the transfected miRNAs. By setting the cut-off value of binding energy to −12, the miRanda algorithm predicted 1076 regulatory target genes for miR-1 (transcripts corresponding to the same gene are combined). We divide the genes into a target gene set and a non-target gene set of miR-1. To introduce additional prediction errors, we randomly select 5%, 10%, 20%, 30%, 40% and 50% genes from the target gene set, set their miR-1 binding scores to 0s and assign their original binding scores to an equal number of randomly selected non-target genes. In other words, we swap the binding sores of a certain percentage of genes in miR-1 target and non-target gene sets. We then calculate the AC score of miR-1 in the expression change profile at 12 h and 24 h after miR-1 transfection based on the perturbed binding affinity data. For each percentage, we repeat the above procedure 100 times. The resulting average AC scores of miR-1 at each perturbing percentage and their p-values are shown in Figure 3.


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

Cheng C, Li LM - PLoS ONE (2008)

Average AC scores and p-values for miR-1 in the miR-1 transfection profile at 12 h (A and B) and 24 h (C and D) based on perturbed miR-1 target prediction scores.The x-axis shows the perturbing percentage from 0% to 50%. At each perturbing percentage, 100 perturbed binding score profiles of miR-1 are produced by exchanging the binding scores of target and non-target genes of miR-1. The standard deviations of AC scores and p-values are also shown.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0001989-g003: Average AC scores and p-values for miR-1 in the miR-1 transfection profile at 12 h (A and B) and 24 h (C and D) based on perturbed miR-1 target prediction scores.The x-axis shows the perturbing percentage from 0% to 50%. At each perturbing percentage, 100 perturbed binding score profiles of miR-1 are produced by exchanging the binding scores of target and non-target genes of miR-1. The standard deviations of AC scores and p-values are also shown.
Mentions: It is known that in silico miRNA target prediction is usually not accurate. Depending on the cut-off setting, the false positive rate and/or the false negative rate of the target predictions could be fairly high. Nonetheless, our method achieves accurate inference of miRNA activity modification in the miRNA transfection data as shown above. To investigate the robustness of our method to the false miRNA target predictions, we introduce additional errors to the miRNA target prediction data and examine whether our method is still able to identify the activity enhancement of the transfected miRNAs. By setting the cut-off value of binding energy to −12, the miRanda algorithm predicted 1076 regulatory target genes for miR-1 (transcripts corresponding to the same gene are combined). We divide the genes into a target gene set and a non-target gene set of miR-1. To introduce additional prediction errors, we randomly select 5%, 10%, 20%, 30%, 40% and 50% genes from the target gene set, set their miR-1 binding scores to 0s and assign their original binding scores to an equal number of randomly selected non-target genes. In other words, we swap the binding sores of a certain percentage of genes in miR-1 target and non-target gene sets. We then calculate the AC score of miR-1 in the expression change profile at 12 h and 24 h after miR-1 transfection based on the perturbed binding affinity data. For each percentage, we repeat the above procedure 100 times. The resulting average AC scores of miR-1 at each perturbing percentage and their p-values are shown in Figure 3.

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