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By Althaus E.

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J vp ∈V j and perform the associated reduction of the gap variables in O(n3 ) time. Moreover, we can further reduce the number of alignment variables by computing j the following tighter upper bound. Assume edge e = {vli , vm } is realized. For every h h string h, there must be a node vp ∈ V such that all characters before sph are aligned j only with characters before sli in string i as well as with characters before sm in string i h are aligned only with characters after sl+1 in string i as j , and all characters after sp+1 j well as with characters after sm+1 in string j .

Biol. 215, 403–410 (1990) 3. : Potential function methods for approximately solving linear programming problems, Theory and Practice. Kluwer Academic Publishers, Boston, 2002 4. : Compact vs exponential-size lp relaxations. Operations Research Letters 30, 57–65 (2002) 5. : Compact optimization can outperform separation: A case study in structural proteomics. 4OR 2, 221–233 (2004) 6. : The multiple sequence alignment problem in biology. SIAM J. Appl. Math. 48 (5), 1073–1082 (1988) 7. : A model of evolutionary change in proteins.

0]; 1 92 10:30:51. 0]; 1 30 10:37:44. 3]; 1 31 10:00:06. 3. Comparison with other programs We compared our algorithm with the most recent structural alignments algorithms, considering the overall best performing programs from the survey [27]: PRRP, ClustalX, and Dialign, together with a recently published program T-Coffee [22], which generally outperforms the other programs. The source code of all these programs was downloaded and they were run with default parameters. We disregarded all other programs, since they perform significantly worse (see [27]).

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A branch-and-cut algorithm for multiple sequence alignment by Althaus E.

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