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BioMed Research International
Volume 2015, Article ID 546763, 9 pages
http://dx.doi.org/10.1155/2015/546763
Research Article

MatPred: Computational Identification of Mature MicroRNAs within Novel Pre-MicroRNAs

1Institute of Biomedical Engineering, College of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang 150001, China
2Bioinformatics Research Center, College of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang 150001, China
3Network Information Center, Qiqihar University, No. 42, Wenhua Street, Qiqihar, Heilongjiang 161006, China

Received 26 July 2015; Revised 18 September 2015; Accepted 28 September 2015

Academic Editor: Mouldy Sioud

Copyright © 2015 Jin Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement. Methodology/Principal Findings. Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods. Conclusions. MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.