Common features of microRNA target prediction tools

By Sarah M. Peterson, Jeffrey A. Thompson, M. Ufkin, P. Sathyanarayana, L. Liaw, Clare Bates Congdon. Frontiers in Genetics, 2014

475 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/492ba458e9791f2c328b376a3dc505460795b160

Citations in the corpus

• deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks (2016, unknown): “While considering CTSs to predict targets of given miRNAs, there are four types of seed sequence matches [37] often referred to as 6mer, 7mer-m8, 7mer-A1, and 8mer in the literature.”