microRNAs are noncoding RNAs which downregulate a large number of target mRNAs and modulate cell activity. Despite continued progress, bioinformatics prediction of microRNA targets remains a challenge since available software still suffer from a lack of accuracy and sensitivity. Moreover, these tools show fairly inconsistent results from one another. Thus, in an attempt to circumvent these difficulties, we aggregated all human results of four important prediction algorithms (miRanda, PITA, SVmicrO, and TargetScan) showing additional characteristics in order to rerank them into a single list. Instead of deciding which prediction tool to use, our method clearly helps biologists getting the best microRNA target predictions from all aggregated databases. The resulting database is freely available through a webtool called miRabel 1 which can take either a list of miRNAs, genes, or signaling pathways as search inputs. Receiver operating characteristic curves and precision-recall curves analysis carried out using experimentally validated data and very large data sets show that miRabel significantly improves the prediction of miRNA targets compared to the four algorithms used separately. Moreover, using the same analytical methods, miRabel shows significantly better predictions than other popular algorithms such as MBSTAR, miRWalk, ExprTarget and miRMap. Interestingly, an Fscore analysis revealed that miRabel also significantly improves the relevance of the top results. The aggregation of results from different databases is therefore a powerful and generalizable approach to many other species to improve miRNA target predictions. Thus, miRabel is an efficient tool to guide biologists in their search for miRNA targets and integrate them into a biological context.
24microRNAs are non-coding RNAs which down-regulate a large number of target 25 mRNAs and modulate cell activity. Despite continued progress, bioinformatics 26 prediction of microRNA targets remains a challenge since available softwares still 27 suffer from a lack of accuracy and sensitivity. Moreover, these tools show fairly 28 inconsistent results from one another. Thus, in an attempt to circumvent these 29 difficulties, we aggregated all human results of three important prediction algorithms 30 (miRanda, PITA and SVmicrO) showing additional characteristics in order to rerank 31 them into a single list. This database is freely available through a webtool called 32 miRabel (http://bioinfo.univ-rouen.fr/mirabel/) which can take either a list of miRNAs, 33 genes or signaling pathways as search inputs. Receiver Operating Characteristic 34 curves and Precision-Recall curves analysis carried out using experimentally validated 35 data and very large datasets show that miRabel significantly improves the prediction 36 of miRNA targets compared to the three algorithms used separatly. Moreover, using 37 the same analytical methods, miRabel shows significantly better predictions than other 38 popular algorithms such as MBSTAR and miRWalk. Interestingly, a F-score analysis 39 revealed that miRabel also significantly improves the relevance of the top results. The 40 aggregation of results from different databases is therefore a powerful and 41 generalizable approach to many other species to improve miRNA target predictions. 42
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