Analyzing Modular RNA Structure Reveals Low Global Structural Entropy in MicroRNA Sequence

Timothy I. Shaw*, Amir Manzour, Yingfeng Wang, Russell L. Malmberg, Liming Cai

Institute of Bioinformatics, University of Georgia Athens, Ga 30605, USA. gatech@uga.edu

Proc LSS Comput Syst Bioinform Conf. August, 2010. Vol. 9, p. 146-155. Full-Text PDF

*To whom correspondence should be addressed.

Secondary structure remains the most exploitable feature for non-coding RNA (ncRNA) gene finding in genomes. However, methods based on secondary structure prediction may generate superfluous amount of candidates for validation and have yet to deliver the desired performance that can complement experimental efforts in ncRNA gene finding. This paper investigates a novel method, unpaired structural entropy (USE) as a measurement for the structure fold stability of ncRNAs. USE proves to be effective in identifying from the genome background a class of ncRNAs, such as precursor microRNAs (pre-miRNAs) that contains a long stem hairpin loop. USE correlates well and performs better than other measures on pre-miRNAs, including the previously formulated structural entropy. As an SVM classifier, USE outperforms existing pre-miRNA classifiers. A long stem hairpin loop is common for a number of other functional RNAs including introns splicing hairpins loops and intrinsic termination hairpin loops. We believe USE can be further applied in developing ab initio prediction programs for a larger class of ncRNAs.