MicroRNAfold: pre-microRNA secondary structure prediction based on modified NCM model with thermodynamics-based scoring strategy

Authors:
Dianwei Han;Jun Zhang;Guiliang Tang
Affiliations:
Department of Computer Science, University of Kentucky, Lexington, KY 40506-0046, USA;Department of Computer Science, University of Kentucky, Lexington, KY 40506-0046, USA;Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY 40546-0236, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2012

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Abstract

An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. Our experimental results show that microRNAfold outperforms the current leading prediction tools in terms of True Negative rate, False Negative rate, Specificity, and Matthews coefficient ratio.