Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses

Arthur C Oliveira,Marcos E Herkenhoff,Luiz A Bovolenta,Ney Lemke,Danillo Pinhal,Pedro G Nachtigall

doi:10.3389/fgene.2017.00059

Arthur C Oliveira, Marcos E Herkenhoff + Show 4 more

Open Access

https://doi.org/10.3389/fgene.2017.00059

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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 in silico prediction analysis of miRNA 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 miRNA-target interactions.

Highlights

MicroRNAs are a large class of small non-coding RNAs [∼22 nucleotides] that post-transcriptionally regulate gene expression
The number of targets predicted by TS, MR, and PT (99 validated and 2 non-validated) was equivalent to the number predicted by all tools together (97 validated and 2 non-validated) (Figure 1A)
Current versions of the miRNA target prediction tools evaluated in this study possess high specificity and precision, generating results with negligible false positive rate

Summary

Introduction

MicroRNAs (miRNAs) are a large class of small non-coding RNAs [∼22 nucleotides (nts)] that post-transcriptionally regulate gene expression They were first identified in the context of Caenorhabditis elegans development (Lee et al, 1993), and they are known to regulate most biological process in animals, plants, and even certain viruses (Lee et al, 1993; Sunkar et al, 2005; Jia et al, 2008). Their function ranges from cellular proliferation and differentiation to response to environmental stimuli and diseases such as cancer (Qiu et al, 2012; Shenoy and Blelloch, 2014; Reddy, 2015). In addition to the seed-based interactions, recent studies reported miRNA regulation through non-seed interactions, demonstrating that the 3 region of the miRNA transcript might be important as the seed sequence for securing target recognition (Tay et al, 2008; Nelson et al, 2011; Chi et al, 2012; Clarke et al, 2012; Broughton et al, 2016)

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Discussion

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