Target prediction for animal microRNAs (miRNAs) has been hindered by the small number of verified targets available to evaluate the accuracy of predicted miRNA-target interactions. Recently, a dataset of 3,404 miRNA-associated mRNA transcripts was identified by immunoprecipitation of the RNA-induced silencing complex components AIN-1 and AIN-2. Our analysis of this AIN-IP dataset revealed enrichment for defining characteristics of functional miRNA-target interactions, including structural accessibility of target sequences, total free energy of miRNA-target hybridization and topology of base-pairing to the 5′ seed region of the miRNA. We used these enriched characteristics as the basis for a quantitative miRNA target prediction method, miRNA targets by weighting immunoprecipitation-enriched parameters (mirWIP), which optimizes sensitivity to verified miRNA-target interactions and specificity to the AIN-IP dataset. MirWIP can be used to capture all known conserved miRNA-mRNA target relationships in Caenorhabditis elegans at a lower false-positive rate than can the current standard methods.
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We thank C. Hammell and all members of the Ambros lab for useful discussions and the Computational Molecular Biology and Statistics Core at the Wadsworth Center for providing computing resources for this work. This research was supported by US National Institutes of Health grants GM34028 and GM066826 to V.A., GM068726 to Y.D. and GM47869 to M. Han as well as US National Science Foundation grant DBI-0650991 to Y.D.; and the Howard Hughes Medical Institute, of which M. Han is an investigator.
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Hammell, M., Long, D., Zhang, L. et al. mirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein–enriched transcripts. Nat Methods 5, 813–819 (2008). https://doi.org/10.1038/nmeth.1247
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