Research article
Accurate microRNA target prediction correlates with protein repression levels
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* Corresponding authors: Manolis Maragkakis maragkakis@fleming.gr - Artemis G Hatzigeorgiou artemis@fleming.gr
- † Equal contributors
1 Institute of Molecular Oncology, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
2 Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle, Germany
3 School of Biology, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
4 Synaptic Ltd., Heraklion, Greece
5 Institute for the Management of Information Systems, "Athena" Research Center, Athens, Greece
6 Knowledge and Database Systems Lab, Department of Computer Science, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
7 Computing Systems Laboratory, Department of Computer Science, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
8 Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20876, USA
9 Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, PA, USA
BMC Bioinformatics 2009, 10:295 doi:10.1186/1471-2105-10-295
Published: 18 September 2009Abstract
Background
MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease.
Results
DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction.
Conclusion
Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT webcite