The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.
Keywords: microRNA, target prediction, seed match, conservation, free energy, site accessibility, machine learning, computational approaches
Citation: Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L and Congdon CB (2014) Common features of microRNA target prediction tools. Front. Genet. 5:23. doi: 10.3389/fgene.2014.00023
Received: 22 November 2013; Accepted: 23 January 2014;
Published online: 18 February 2014.
Edited by:Michael Ochs, The College of New Jersey, USA
Reviewed by:Subha Madhavan, Georgetown University, USA
Copyright © 2014 Peterson, Thompson, Ufkin, Sathyanarayana, Liaw and Congdon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Clare Bates Congdon, Department of Computer Science, University of Southern Maine, 96 Falmouth Street, Portland, ME 04104-9300, USA e-mail: firstname.lastname@example.org