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Open Access Methodology article

RNAalifold: improved consensus structure prediction for RNA alignments

Stephan H Bernhart1*, Ivo L Hofacker2, Sebastian Will3, Andreas R Gruber2 and Peter F Stadler1245

Author Affiliations

1 Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany

2 Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria

3 Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee, Geb. 106, D-79110 Freiburg, Germany

4 RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology (IZI) Perlickstrasse 1, D-04103 Leipzig, Germany

5 The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, New Mexico

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BMC Bioinformatics 2008, 9:474  doi:10.1186/1471-2105-9-474

Published: 11 November 2008

Abstract

Background

The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.

Results

We show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets.

Conclusion

The new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.