MicroRNA target prediction based on second-order Hidden Markov Model

Abstract

MicroRNAs are one class of small single-stranded RNA of about 22nt serving as important negative gene regulators. In animals, miRNAs mainly repress protein translation by binding itself to the 3’ UTR regions of mRNAs with imperfect complementary pairing. Although bioinformatics investigations have resulted in a number of target prediction tools, all of these have a common shortcoming—a high false positive rate. Therefore, it is important to further filter the predicted targets. In this paper, based on miRNA:target duplex, we construct a second-order Hidden Markov Model, implement Baum-Welch training algorithm and apply this model to further process predicted targets. The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%, specificity of 55.10% and accuracy of 69.62% by 10-fold cross-validation experiments. In order to further verify the applicability of the algorithm, previously collected datasets, including 195 positive and 38 negative, are chosen to test it, with consistent results. We believe that our method will provide some guidance for experimental biologists, especially in choosing miRNA targets for validation.

Keywords

microRNA / target gene / experimentally supported targets / second-order Hidden Markov Model / forward algorithm