Performance and evaluation of microRNA gene identification tools

Abstract

MicroRNAs are small single stranded RNA molecules of ~ 22 nt in length which play important role in post transcriptional gene regulation either by translational repression of mRNA or by their cleavage. Since their discovery, continuous efforts to identify the miRNA genes led to the discovery of several miRNAs in plants as well as animals. Owing to the limitations of the molecular genetic techniques of miRNA identification, computational approaches were introduced for better and affordable in silico-miRNA predictions. Here, we compared a few miRNA gene identification tools, such as 'MiPred','Triplet-SVM','BayesMiRNAfind','OneClassmiRNAfind'and 'BayesSVMmiRNAfind' to evaluate the performance of its predictability based on the real and pseudo precursor miRNA datasets. Of all the tools examined MiPred is more sensitive (96%) in identifying pseudo miRNAs than Triplet-SVM for real/pseudo miRNA classification, whereas for mature miRNA prediction 'one-class' SVM classifier shows best specificity (96%), while BayesSVMmiRNAfind shows least specificity (8%). © 2009 Orchard S.