Research article

Strategies for measuring evolutionary conservation of RNA secondary structures

Andreas R Gruber¹ , Stephan H Bernhart^1,2 , Ivo L Hofacker¹ and Stefan Washietl^1,3

¹Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria

²Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, D-04109 Leipzig, Germany

³EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

author email corresponding author email

BMC Bioinformatics 2008, 9:122doi:10.1186/1471-2105-9-122

Published:	26 February 2008

Abstract

Background

Evolutionary conservation of RNA secondary structure is a typical feature of many functional non-coding RNAs. Since almost all of the available methods used for prediction and annotation of non-coding RNA genes rely on this evolutionary signature, accurate measures for structural conservation are essential.

Results

We systematically assessed the ability of various measures to detect conserved RNA structures in multiple sequence alignments. We tested three existing and eight novel strategies that are based on metrics of folding energies, metrics of single optimal structure predictions, and metrics of structure ensembles. We find that the folding energy based SCI score used in the RNAz program and a simple base-pair distance metric are by far the most accurate. The use of more complex metrics like for example tree editing does not improve performance. A variant of the SCI performed particularly well on highly conserved alignments and is thus a viable alternative when only little evolutionary information is available. Surprisingly, ensemble based methods that, in principle, could benefit from the additional information contained in sub-optimal structures, perform particularly poorly. As a general trend, we observed that methods that include a consensus structure prediction outperformed equivalent methods that only consider pairwise comparisons.

Conclusion

Structural conservation can be measured accurately with relatively simple and intuitive metrics. They have the potential to form the basis of future RNA gene finders, that face new challenges like finding lineage specific structures or detecting mis-aligned sequences.