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Bioinformatics Advance Access published online on November 6, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp598
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Identification of microRNA activity by Targets' Reverse EXpression

Stefano Volinia 1,2,3, Rosa Visone 3, Marco Galasso 1, Elda Rossi 4 and Carlo M Croce 3,*

1DAMA, Data Mining for Analysis of Microarrays, Dept of Morphology and Embryology, University of Ferrara, Italy.
2Biomedical Informatics, Ohio State University, Columbus, OH .
3Comprehensive Cancer Center, Ohio State University, Columbus, OH.
4CINECA, Bologna, Italy

*To whom correspondence should be addressed. Carlo M Croce


  Abstract

Motivation: Non-coding miRNAs act as regulators of global protein output. While their major effect is on protein levels of target genes, it has been proven that they also specifically impact on the messenger RNA level of targets. Prominent interest in microRNAs strongly motivates the need for increasing the options available to detect their cellular activity.

Results: We used the effect of miRNAs over their targets for the detection of miRNA activity using mRNAs expression profiles. Here we describe the method, called T-REX (from Targets' Reverse EXpression), compare it to other similar applications, show its effectiveness and apply it to build activity maps. We used six different target predictions from each of four algorithms: TargetScan, PicTar, DIANA-microT and DIANA Union.

First, we proved the sensitivity and specificity of our technique in miRNA over-expression and knock-out animal models. Then, we used whole transcriptome data from acute myeloid leukemia to show that we could identify critical miRNAs in a real life, complex, clinically relevant dataset. Finally, we studied sixty-six different cellular conditions to confirm and extend the current knowledge on the role of miRNAs in cellular physiology and in cancer.

Associate Editor: Prof. Ivo Hofacker

Received on June 25, 2009; revised on October 14, 2009; accepted on October 15, 2009

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