A time-efficient, linear-space local similarity algorithm
Advances in Applied Mathematics
A polyhedral approach to sequence alignment problems
Discrete Applied Mathematics - Special volume on combinatorial molecular biology
Structural alignment of large—size proteins via lagrangian relaxation
Proceedings of the sixth annual international conference on Computational biology
Alignment of RNA base pairing probability matrices
Bioinformatics
Multiple structural RNA alignment with lagrangian relaxation
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Structural alignment of two RNA sequences with lagrangian relaxation
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
An Efficient Lagrangian Relaxation for the Contact Map Overlap Problem
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
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During the last few years new functionalities of RNA have been discovered, renewing the need for computational tools for their analysis. To this respect, multiple sequence alignment is an essential step in finding structurally conserved regions in related RNA sequences. In contrast to proteins, many classes of functionally related RNA molecules show a rather weak sequence conservation but instead a fairly well conserved secondary structure. Hence, any method that relates RNA sequences in form of multiple alignments should take structural features into account, which has been verified in recent studies. Progress has been made in developing new structural alignment algorithms, however, current methods are computationally costly or do not have the desired accuracy to make them an everyday tool. In this paper we present a fast, practical, and accurate method for computing multiple, structural RNA alignments. The method is based on combining a new pairwise structural alignment method with the popular program T-Coffee. Our pairwise method is based on an integer linear programming (ILP) formulation resulting from a graph-theoretic reformulation of the structural alignment problem. We find provably optimal or near-optimal solutions of the ILP with a Lagrangian approach. Tests on a recently published benchmark set show that our Lagrangian approach outperforms current programs in quality and in the length of the sequences it can align.