miRRim

The identification of novel miRNAs has significant biological and clinical importance. However, none of the known miRNA features alone is sufficient for accurately detecting novel miRNAs. The aim of this paper is to integrate these features in a straightforward manner for detecting miRNAs with better accuracy. Since most miRNA regions are highly conserved among vertebrates for the ability to form stable hairpin structures, we implemented a hidden Markov model that outputs multidimensional feature vectors composed of both evolutionary features and secondary structural ones. The proposed method, called miRRim, outperformed existing ones in terms of detection/prediction performance: The total number of predictions was smaller than with existing methods when the number of miRNAs detected was adjusted to be the same. Moreover, there were several candidates predicted only by our method that are clustered with the known miRNAs, suggesting that our method is able to detect novel miRNAs. Genomic coordinates of predicted miRNA can be obtained from http://mirrim.ncrna.org/.[1]

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