Comparative Analysis of miRNA-Target Prediction Algorithms with Experimentally Positive Data in C. elegans and R. norvegicus Genomes

Abstract

MicroRNAs (miRNAs) are small non-encoding RNAs of 19-24 nucleotides long. It regulates gene expression through target mRNA degradation or translational gene silencing. Experimental based prediction is laborious and economically unfavorable due to a huge number of miRNAs and potential targets. So researchers are focused on computational approach for faster prediction. A large number of computational based prediction tools have been developed, but their results are often inconsistent. Hence, finding a reliable computational based prediction tool is still a challenging task. Here we proposed a computational method, microTarget for finding miRNA - mRNA target interactions. We validated our result in C. elegans and Rattus norvegicus genomes and compared performance with three computational methods, like miRanda, PITA, and RNAhybrid. Signal-to-noise ratio, z score, Receiver operating characteristic (ROC) curve analysis, Matthews correlation coefficient (MCC) and F measure show that microTarget exhibits good performance than other three miRNA - mRNA target interactions methods used in this study.

Keywords

• mirna - mrna target interactions
• target validation
• complementarity score
• computational methods.