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• W2614523924 abstract "Target prediction is generally the first step towards recognition of bona fide microRNA-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single microRNA or a subset of microRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature—TargetScan, miRanda-mirSVR, Pita, and RNA22, and we determined the most effective approach for analyzing target prediction data (individual, union or intersection). For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 microRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p) and 1,400 genes (700 validated and 700 non-validated) as targets of these microRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TargetScan/miRanda-mirSVR and TargetScan/miRanda-mirSVR/RNA22 enhanced the quality of in silico prediction analysis of microRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of microRNA-target interactions." @default.
• W2614523924 created "2017-05-26" @default.
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• W2614523924 date "2017-05-16" @default.
• W2614523924 modified "2023-10-16" @default.
• W2614523924 title "Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses" @default.
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• W2614523924 doi "https://doi.org/10.3389/fgene.2017.00059" @default.
• W2614523924 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5432626" @default.
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• W2614523924 hasPublicationYear "2017" @default.
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