Memory-efficient A* Heuristics for multiple sequence alignment

Proceedings of the National Conference on Artificial Intelligence

Volumn , Issue , 2002, Pages 737-743

  McNaughton, Matthew ^a   Lu, Paul ^a   Schaeffer, Jonathan ^a   Szafron, Duane ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COSTS; DNA; HEURISTIC METHODS; PROBLEM SOLVING; PROTEINS; TREES (MATHEMATICS);

MULTIPLE SEQUENCE ALIGNMENT;

ARTIFICIAL INTELLIGENCE;

EID: 0036923125     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Altschul, S. F. 1989. Gap costs for multiple sequence alignment. J. of Theoretical Biology 138:297-309.
2. Carrillo, H., and Lipman, D. 1988. The multiple sequence alignment problem in biology. SIAM J. on Applied Mathematics 48(5): 1073-1082.
3. Chao, K.; Hardison, R.; and Miller, W. 1994. Recent developments in linear-space alignment methods: A survey. J. of Computational Biology 1(4):271-291.
4. Culberson, J., and Schaeffer, J. 1998. Pattern databases. Computational Intelligence 14(3):318-334.
5. Dayhoff, M. O.; Schwarte, R. M.; and Orcutt, B. C. 1979. A model of evolutionary change in proteins. In Dayhoff, M. O., ed., Atlas of Protein Structure, volume 5(Suppl. 3). Silver Spring, Md.: National Biomedical Reasearch Foundataion. 345-352.
6. Gallin, W. 2001. A selection of potassium channel protein sequences. http://www.cs.ualberta.ca/bioinfo/sequences/pchannel/.
7. Hirschberg, D. 1975. A linear space algorithm for computing maximal common subexpressions. CACM 18(6):341-343.
8. Holte, R. 1996. Hierarchical A*: Searching abstraction hierarchies efficiently. AAAI 530-535.
9. Junghanns, A., and Schaeffer, J. 2001. Enhancing single-agent search using domain knowledge. Artificial Intelligence 129(1-2):219-251.
10. Korf, R., and Reid, M. 1998. Complexity analysis of admissible heuristic search. AAAI 305-310.
11. Korf, R., and Zhang, W. 2000. Divide-and-conquer frontier search applied to optimal sequence alignment. AAAI 910-916.
12. Korf, R. 1999. Divide-and-conquer bidirectional search: First results. IJCAI 1184-1189.
13. Korf, R. 2000. Recent progress in the design and analysis of admissible heuristic functions. AAAI 1165-1170.
14. Myers, E., and Miller, W. 1988. Optimal alignments in linear space. CABIOS 4(1): 11-17.
15. Needleman, S., and Wunsch, C. 1970. A general method applicable to the search for similarities in the amino acid sequences of two proteins. J. of Molecular Biology 48:443-453.
16. Reinert, K., and Lermen, M. 2000. The practical use of the A* algorithm for exact multiple sequence alignment. J. of Computational Biology 7(5):655-671.
17. Reinert, K.; Stoye, J.; and Will, T. 2000. An iterative method for faster sum-of-pairs multiple sequence alignment. Bioinformatics 16(9):808-814.
18. Spouge, J. 1989. Speeding up dynamic programming algorithms for finding optimal lattice paths. SIAM J. of Applied Mathematics 49(5): 1552-1566.
19. Thompson, J.; Higgins, D.; and Gibson, T. 1994. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22:4673-4680.
20. Thompson, J.; Plewniak, R; and Poch, O. 1999. BA1-iBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics 15(1):87-88.
21. Yoshizumi, T.; Miura, T.; and Ishida, T. 2000. A* with partial expansion for large branching factor problems. AAAI 923-929.