Affordable Access

Publisher Website

RNAalifold: improved consensus structure prediction for RNA alignments

Authors
Journal
BMC Bioinformatics
1471-2105
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
9
Issue
1
Identifiers
DOI: 10.1186/1471-2105-9-474
Keywords
  • Methodology Article
Disciplines
  • Computer Science

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.

There are no comments yet on this publication. Be the first to share your thoughts.