Sung-Kyu Kim12†, Jin-Wu Nam12†, Je-Keun Rhee12, Wha-Jin Lee12 and Byoung-Tak Zhang123*
† Equal contributors
1 Graduate Program in Bioinformatics, Seoul National University, Seoul, Korea
2 Center for Bioinformation Technology (CBIT), Seoul National University, Seoul, Korea
3 Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, Korea
* Corresponding author: Byoung-Tak Zhang
MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes.
We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program.
miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.