Prediction of miRNA targets

Abstract

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of largescale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defi ned rules in order to build models based on experimentally verifi ed miRNA targets. Largescale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, effi ciency in prediction of miRNA targets that are concurrently verifi ed experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specifi c tools and their effi cacy in extracting biologically signifi cant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specifi c tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.