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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Genomics

Open Access Proceedings

Simultaneous prediction of RNA secondary structure and helix coaxial stacking

Pooya Shareghi1*, Yingfeng Wang1, Russell Malmberg23 and Liming Cai12*

Author Affiliations

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

2 Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA

3 Department of Plant Biology, University of Georgia, Athens, GA 30602, USA

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BMC Genomics 2012, 13(Suppl 3):S7  doi:10.1186/1471-2164-13-S3-S7

Published: 11 June 2012

Abstract

Background

RNA secondary structure plays a scaffolding role for RNA tertiary conformation. Accurate secondary structure prediction can not only identify double-stranded helices and single stranded-loops but also help provide information for potential tertiary interaction motifs critical to the 3D conformation. The average accuracy in ab initio prediction remains 70%; performance improvement has only been limited to short RNA sequences. The prediction of tertiary interaction motifs is difficult without multiple, related sequences that are usually not available. This paper presents research that aims to improve the secondary structure prediction performance and to develop a capability to predict coaxial stacking between helices. Coaxial stacking positions two helices on the same axis, a tertiary motif present in almost all junctions that account for a high percentage of RNA tertiary structures.

Results

This research identified energetic rules for coaxial stacks and geometric constraints on stack combinations, which were applied to developing an efficient dynamic programming application for simultaneous prediction of secondary structure and coaxial stacking. Results on a number of non-coding RNA data sets, of short and moderately long lengths, show a performance improvement (specially on tRNAs) for secondary structure prediction when compared with existing methods. The program also demonstrates a capability for prediction of coaxial stacking.

Conclusions

The significant leap of performance on tRNAs demonstrated in this work suggests that a breakthrough to a higher performance in RNA secondary structure prediction may lie in understanding contributions from tertiary motifs critical to the structure, as such information can be used to constrain geometrically as well as energetically the space of RNA secondary structure.