MOTIVATION: Non-coding microRNAs (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 miRNAs 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 66 different cellular conditions to confirm and extend the current knowledge on the role of miRNAs in cellular physiology and in cancer. AVAILABILITY: Software is available at http://aqua.unife.it and is free for all users with no login requirement.
Identification of microRNA activity by Targets' Reverse EXpression.
VOLINIA, Stefano;GALASSO, Marco;CROCE, Carlo Maria
2010
Abstract
MOTIVATION: Non-coding microRNAs (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 miRNAs 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 66 different cellular conditions to confirm and extend the current knowledge on the role of miRNAs in cellular physiology and in cancer. AVAILABILITY: Software is available at http://aqua.unife.it and is free for all users with no login requirement.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
