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Computational Prediction of microRNA Targets

  • Chapter
microRNA: Basic Science

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 887))

Abstract

Computational prediction of microRNA (miRNA) targets is a fundamental step towards the characterization of miRNA function and the understanding of their role in disease. A single miRNA can regulate hundreds of different gene transcripts through partial sequence complementarity and a single gene may be regulated by several miRNAs acting cooperatively. The remarkable advances made in recent years have allowed the identification of key features for functional miRNA binding sites. A plethora of prediction tools are now available, but their accuracies remain rather poor, as miRNA target recognition has revealed itself to be a very complex and dynamic mechanism, still only partially understood.

In this chapter, the principles of miRNA target prediction in animals are presented, together with the most up-to-date and effective computational approaches and tools available.

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Notes

  1. 1.

    Bulges are unpaired stretches of nucleotides located within one strand of a nucleic acid duplex.

  2. 2.

    miRNAs encoded within the introns of coding genes.

  3. 3.

    ceRNA are coding or noncoding transcripts that regulate other transcripts by competing for shared miRNAs.

  4. 4.

    The gene level test consisted in the prediction of interaction between mRNAs and a given miRNA. The duplex level test consisted in the prediction of interaction between a given fragment of mRNA and a given miRNA.

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  1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L Levy Pl, New York, NY, 10029, USA

    Alessandro Laganà

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  1. Columbia University Medical Center, New York, New York, USA

    Gaetano Santulli

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Laganà, A. (2015). Computational Prediction of microRNA Targets. In: Santulli, G. (eds) microRNA: Basic Science. Advances in Experimental Medicine and Biology, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-22380-3_12

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