Computational Tools for Genome-Wide miRNA Prediction and Study



Tareq B. Malas1, Timothy Ravasi2, *
1 Division of Biological and Environmental Sciences & Engineering, Division of Applied Mathematics and Computer Sciences, Computational Biosciences Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
2 King Abdullah University of Science and Technology Thuwal 23955 Kingdom of Saudi Arabia. Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA


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© 2012 Malas and Ravasi

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the King Abdullah University of Science and Technology Thuwal 23955 Kingdom of Saudi Arabia. Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA; Tel: +966-5-44700067, Fax: +966-2-8020127; E-mail: timothy.ravasi@kaust.edu.sa


Abstract

MicroRNAs (miRNAs) are single-stranded non-coding RNA susually of 22 nucleotidesin length that play an important post-transcriptional regulation role in many organisms. MicroRNAs bind a seed sequence to the 3'-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein level. This regulatory mechanism is central to many biological processes and perturbation could lead to diseases such as cancer. Given the biological importance, of miRNAs, there is a great need to identify and study their targets and functions. However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. Next-generation sequencing coupled with new computational tools has recently evolved to help researchers efficiently identify large numbers of novel miRNAs. In this review, we describe recent miRNA prediction tools and discuss their priorities, advantages and disadvantages.

Keywords: miRNAs, Computational prediction tools, Gene regulation, Biological databases, Genomics.