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Front Genet. 2014 Feb 18;5:23. doi: 10.3389/fgene.2014.00023. eCollection 2014.

Common features of microRNA target prediction tools.

Author information

1
Center for Molecular Medicine, Maine Medical Center Research Institute Scarborough, ME, USA ; Graduate School of Biomedical Sciences and Engineering, University of Maine Orono, ME, USA.
2
Department of Computer Science, University of Southern Maine Portland, ME, USA.
3
Graduate School of Biomedical Sciences and Engineering, University of Maine Orono, ME, USA ; Department of Computer Science, University of Southern Maine Portland, ME, USA.

Abstract

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:

computational approaches; conservation; free energy; machine learning; microRNA; seed match; site accessibility; target prediction

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