Micro Target: MicroRNA Target Prediction and Validation with Experimentally Positive and Negative Examples

Abstract

MicroRNAs (miRNAs) usually controls the gene by binding to complementary sites of 3’ untranslated region of its target genes. Numerous criteria-based and machine learning approaches are available in the literature to predict miRNA–mRNA interactions, but most of them struggle with either high false positive or false negative rates and also don’t show good validation with experimentally validated positive and negative examples. Here we present microTarget, a new computational approach for identifying miRNA target genes which are based on complementarity score, thermodynamic duplex stability and also independent of conservation of target sites in related genomes. In this article, we validated our algorithm using positive and negative data from the literature in various human tissues, and our method outperformed existing computational methods such as miRanda, RNA22, and PITA. Receiver operating characteristic curves (ROC) and Matthew's correlation coefficient (MCC) were calculated using experimentally validated data, and they reveal that microTarget greatly improves miRNA target prediction compared to the three algorithms employed individually. Additionally, an F-score analysis demonstrated that microTarget greatly enhances the relevance of the other techniques. Thus, microTarget is a useful tool for biologists looking for miRNA targets and integrating them into biological contexts.