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RNAalifold. Improved consensus structure prediction for RNA alignments

: Bernhart, Stephan H.; Hofacker, Ivo L.; Will, Sebastian; Gruber, Andreas R.; Stadler, Peter F.

Fulltext urn:nbn:de:0011-n-875887 (398 KByte PDF)
MD5 Fingerprint: 452538a494991eff27bb3b23679aca79
Created on: 28.1.2009

BMC bioinformatics. Online journal 9 (2008), Art. 474, 13 pp.
ISSN: 1471-2105
Journal Article, Electronic Publication
Fraunhofer IZI ()

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.
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.
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.