A scoring matrix approach to detecting miRNA target sites - PubMed (original) (raw)
A scoring matrix approach to detecting miRNA target sites
Simon Moxon et al. Algorithms Mol Biol. 2008.
Abstract
Background: Experimental identification of microRNA (miRNA) targets is a difficult and time consuming process. As a consequence several computational prediction methods have been devised in order to predict targets for follow up experimental validation. Current computational target prediction methods use only the miRNA sequence as input. With an increasing number of experimentally validated targets becoming available, utilising this additional information in the search for further targets may help to improve the specificity of computational methods for target site prediction.
Results: We introduce a generic target prediction method, the Stacking Binding Matrix (SBM) that uses both information about the miRNA as well as experimentally validated target sequences in the search for candidate target sequences. We demonstrate the utility of our method by applying it to both animal and plant data sets and compare it with miRanda, a commonly used target prediction method.
Conclusion: We show that SBM can be applied to target prediction in both plants and animals and performs well in terms of sensitivity and specificity. Open source code implementing the SBM method, together with documentation and examples are freely available for download from the address in the Availability and Requirements section.
Figures
Figure 1
Alignment of the Drosophila melanogaster let-7 miRNA to a cognate target site in the 3' UTR of the ab gene adapted from [21, Fig. 1].
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References
- Carrington JC, Ambros V. Role of microRNAs in plant and animal development. Science. 2003;301:336–338. doi: 10.1126/science.1085242. http://dx.doi.org/10.1126/science.1085242 - DOI - DOI - PubMed
- Calin GA, Croce CM. MicroRNA-cancer connection: the beginning of a new tale. Cancer Res. 2006;66:7390–7394. doi: 10.1158/0008-5472.CAN-06-0800. http://dx.doi.org/10.1158/0008-5472.CAN-06-0800 - DOI - DOI - PubMed
- Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP. MicroRNAs in plants. Genes Dev. 2002;16:1616–1626. doi: 10.1101/gad.1004402. http://dx.doi.org/10.1101/gad.1004402 - DOI - DOI - PMC - PubMed
- Hammond SM, Bernstein E, Beach D, Hannon GJ. An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells. Nature. 2000;404:293–296. doi: 10.1038/35005107. http://dx.doi.org/10.1038/35005107 - DOI - DOI - PubMed
- Mallory AC, Vaucheret H. Functions of microRNAs and related small RNAs in plants. Nat Genet. 2006:S31–S36. doi: 10.1038/ng1791. http://dx.doi.org/10.1038/ng1791 - DOI - DOI - PubMed
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