ArticleFrontiers in genetics2017
Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses.
Article in Frontiers in genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- Genome Reconstruction of Grapevine Virus M and In Silico miRNA Prediction for Sequence-Specific Gene Silencing.Current microbiology · 2025Article
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- Comparative analysis of miRNA-mRNA interaction prediction tools based on experimental head and neck cancer data.Einstein (Sao Paulo, Brazil) · 2025Article
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- Identification of differentially expressed miRNAs involved in vascular aging reveals pathways associated with the endocrine hormone regulation.Biogerontology · 2024Article
- Identification and characterization of host miRNAs that target the mouse mammary tumour virus (MMTV) genome.Open biology · 2024Article
- miR-1202 acts as anti-oncomiR in myeloid leukaemia by down-modulating GATA-1Open biology · 2024Article
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- AnFrontiers in genetics · 2024Article
- MicroRNA Transcriptomes Reveal Prevalence of Rare and Species-Specific Arm Switching Events During Zebrafish Ontogenesis.Evolutionary bioinformatics online · 2024Article
- Circulatory extracellular vesicle derived miR-195-5p promotes cellular apoptosis and suppresses cell proliferation in the buffalo endometrial primary cell culture.Scientific reports · 2023Article
- Insights into Online microRNA Bioinformatics Tools.Non-coding RNA · 2023Review
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- Global and precise identification of functional miRNA targets in mESCs by integrative analysis.EMBO reports · 2022Article
- Learning noncoding RNA biology from viruses.Mammalian genome : official journal of the International Mammalian Genome Society · 2022Review
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- Differences in PLAToxins · 2022Article
- Deciphering miR-520c-3p as a probable target for immunometabolism in non-small cell lung cancer using systems biology approach.Oncotarget · 2022Article
- Machine Learning Using Gene-Sets to Infer miRNA Function.Advances in experimental medicine and biology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA)-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 miRNA or a subset of miRNAs. 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 (TS), miRanda-mirSVR (MR), Pita, and RNA22 (R22), 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 miRNAs (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 miRNAs. 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 TS/MR and TS/MR/R22 enhanced the quality of
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