Journal Article

Lightweight comparison of RNAs based on exact sequence–structure matches

Steffen Heyne, Sebastian Will, Michael Beckstette and Rolf Backofen

in Bioinformatics

Volume 25, issue 16, pages 2095-2102
Published in print August 2009 | ISSN: 1367-4803
Published online February 2009 | e-ISSN: 1460-2059 | DOI:

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Motivation: Specific functions of ribonucleic acid (RNA) molecules are often associated with different motifs in the RNA structure. The key feature that forms such an RNA motif is the combination of sequence and structure properties. In this article, we introduce a new RNA sequence–structure comparison method which maintains exact matching substructures. Existing common substructures are treated as whole unit while variability is allowed between such structural motifs.

Based on a fast detectable set of overlapping and crossing substructure matches for two nested RNA secondary structures, our method ExpaRNA (exact pattern of alignment of RNA) computes the longest collinear sequence of substructures common to two RNAs in O(H·nm) time and O(nm) space, where Hn·m for real RNA structures. Applied to different RNAs, our method correctly identifies sequence–structure similarities between two RNAs.

Results: We have compared ExpaRNA with two other alignment methods that work with given RNA structures, namely RNAforester and RNA_align. The results are in good agreement, but can be obtained in a fraction of running time, in particular for larger RNAs. We have also used ExpaRNA to speed up state-of-the-art Sankoff-style alignment tools like LocARNA, and observe a tradeoff between quality and speed. However, we get a speedup of 4.25 even in the highest quality setting, where the quality of the produced alignment is comparable to that of LocARNA alone.

Availability: The presented algorithm is implemented in the program ExpaRNA, which is available from our website (

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Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6444 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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