Animal microRNA Target Prediction Using Diverse Sequence-Specific determinants

Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.