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This article is part of the supplement: Selected articles from the First IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2011): Bioinformatics

Open Access Research

Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds

Yingfeng Wang1*, Amir Manzour3, Pooya Shareghi1, Timothy I Shaw3, Ying-Wai Li4, Russell L Malmberg23 and Liming Cai13*

Author affiliations

1 Department of Computer Science, University of Georgia, Athens, Georgia 30602, USA

2 Department of Plant Biology, University of Georgia, Athens, Georgia 30602, USA

3 Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, USA

4 Center for Simulational Physics, University of Georgia, Athens, Georgia 30602, USA

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 5):S1  doi:10.1186/1471-2105-13-S5-S1

Published: 12 April 2012



The computational identification of RNAs in genomic sequences requires the identification of signals of RNA sequences. Shannon base pairing entropy is an indicator for RNA secondary structure fold certainty in detection of structural, non-coding RNAs (ncRNAs). Under the Boltzmann ensemble of secondary structures, the probability of a base pair is estimated from its frequency across all the alternative equilibrium structures. However, such an entropy has yet to deliver the desired performance for distinguishing ncRNAs from random sequences. Developing novel methods to improve the entropy measure performance may result in more effective ncRNA gene finding based on structure detection.


This paper shows that the measuring performance of base pairing entropy can be significantly improved with a constrained secondary structure ensemble in which only canonical base pairs are assumed to occur in energetically stable stems in a fold. This constraint actually reduces the space of the secondary structure and may lower the probabilities of base pairs unfavorable to the native fold. Indeed, base pairing entropies computed with this constrained model demonstrate substantially narrowed gaps of Z-scores between ncRNAs, as well as drastic increases in the Z-score for all 13 tested ncRNA sets, compared to shuffled sequences.


These results suggest the viability of developing effective structure-based ncRNA gene finding methods by investigating secondary structure ensembles of ncRNAs.