- Validation of pre-miRNAs predictions
- Prediction of mature miRNAs
MicroRNAs (miRNAs) are short RNA species derived from hairpin-forming miRNA precursors (pre-miRNA) and acting as key post-transcriptional regulators. Most computational tools labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. Sequence and structural features that determine the location of the miRNA, and the extent to which these properties vary from species to species, are poorly understood. We have developed miRdup, a computational predictor for the identification of the most likely miRNA location within a given pre-miRNA or the validation of a candidate miRNA. MiRdup is based on a random forest classifier trained with experimentally validated miRNAs from miRbase, with features that characterize the miRNA-miRNA* duplex. Since we observed that miRNAs have sequence and structural properties that differ between species, mostly in terms of duplex stability, we trained various clade-specific miRdup models and obtained increased accuracy. MiRdup self-trains on the most recent version of miRbase and is easy to use. Combined with existing pre-miRNA predictors, it will be valuable for both de novo mapping of miRNAs and filtering of large sets of candidate miRNAs obtained from transcriptome sequencing projects. MiRdup is open source under the GPL and available at http://www.cs.mcgill.ca/~blanchem/mirdup/.
- Given a miRNA and a pre-miRNA, miRdup validates pre-miRNAs predictions from other tools. To that purpose, it uses a trained model on a particular set of species in order to maximize species-specificity. The model is trained on 100 features with adaboost on random forest.
- Given a pre-miRNA and a model, miRdup predicts a potential miRNA. 


Online version: Wheat MicroRNA Portal


miRdup is available to download here (includes readme and source code): miRdup_1.4.zip (last modification: January 2016)
Readme file: readme.txt
Some models have been trained on miRbase v19: All miRbase, mammals, fishes, nematods, arthropods and plants
Model trained on miRbase v21: All miRbase v21

Leclercq M., Diallo A.B, Blanchette M. (2013)