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
Finding Common Subsequences with Arcs and Pseudoknots
CPM '99 Proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching
Alignment of RNA base pairing probability matrices
Bioinformatics
Structural alignment of two RNA sequences with lagrangian relaxation
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
Fast and accurate structural RNA alignment by progressive lagrangian optimization
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
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Many classes of functionally related RNA molecules show a rather weak sequence conservation but instead a fairly well conserved secondary structure. Hence, it is clear that any method that relates RNA sequences in form of (multiple) alignments should take structural features into account. Since multiple alignments are of great importance for subsequent data analysis, research in improving the speed and accuracy of such alignments benefits many other analysis problems. We present a formulation for computing provably optimal, structure-based, multiple RNA alignments and give an algorithm that finds such an optimal (or near-optimal) solution. To solve the resulting computational problem we propose an algorithm based on Lagrangian relaxation which already proved successful in the two-sequence case. We compare our implementation, mLARA, to three programs (clustalW, MARNA, and pmmulti) and demonstrate that we can often compute multiple alignments with consensus structures that have a significant lower minimum free energy term than computed by the other programs. Our prototypical experiments show that our new algorithm is competitive and, in contrast to other methods, is applicable to long sequences where standard dynamic programming approaches must fail. Furthermore, the Lagrangian method is capable of handling arbitrary pseudoknot structures.