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Tools for Sequence-Based miRNA Target Prediction: What to Choose?

Molecular Pathology Laboratory, Department of Pathology, Faculty of Medicine, Universidad de La Frontera, Avenida Alemania 0458, 3rd Floor, Temuco 4810296, Chile
Scientific and Technological Bioresource Nucleus (BIOREN), Universidad de La Frontera, Avenida Francisco Salazar 01145, Casilla 54-D, Temuco 4811230, Chile
Author to whom correspondence should be addressed.
Academic Editors: Kumiko UI-TEI and Martin Pichler
Int. J. Mol. Sci. 2016, 17(12), 1987;
Received: 14 September 2016 / Revised: 21 November 2016 / Accepted: 22 November 2016 / Published: 9 December 2016
(This article belongs to the Special Issue microRNA Regulation 2017)
PDF [1038 KB, uploaded 9 December 2016]


MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists. View Full-Text
Keywords: miRNA; bioinformatics; prediction tools; TargetScan; DIANA tools; miRanda miRNA; bioinformatics; prediction tools; TargetScan; DIANA tools; miRanda

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Riffo-Campos, Á.L.; Riquelme, I.; Brebi-Mieville, P. Tools for Sequence-Based miRNA Target Prediction: What to Choose? Int. J. Mol. Sci. 2016, 17, 1987.

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