Improved approximation algorithms for tree alignment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1075, Issue , 1996, Pages 220-233

  Wang, Lusheng ^a   Gusfield, Dan ^a  

^a University of California   (United States)

Author keywords

Approximation algorithm; Polynomial time approximation scheme; Tree alignment

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; BIOINFORMATICS; COMPUTATIONAL EFFICIENCY; ERRORS; FORESTRY; PATTERN MATCHING; POLYNOMIAL APPROXIMATION; TREES (MATHEMATICS);

APPROXIMATION METHODS; APPROXIMATION SCHEME; BIOLOGICAL SEQUENCES; COMPUTATIONAL BIOLOGY; GUARANTEED ERROR BOUNDS; MULTIPLE SEQUENCE ALIGNMENTS; POLYNOMIAL TIME APPROXIMATION SCHEMES; TREE ALIGNMENT;

APPROXIMATION ALGORITHMS;

EID: 35248894417     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61258-0_17     Document Type: Conference Paper

References (15)

1. S. Altschul and D. Lipman, Trees, stars, and multiple sequence alignment, SIAM Journal on Applied Math. 49, pp. 197-209, 1989
2. S. C. Chan, A. K. C. Wong and D. K. T. Chiu, A survey of multiple sequence comparison methods, Bulletin of Mathematical Biology 54(4), pp. 563-598, 1992.
3. S.K. Gupta, J. D. Kececioglu, and A. A. Schaffer, Making the shortest-paths approach to sum-of-pairs multiple sequence alignment more space efficient in practice, CPM95, pp. 128-143.
4. D. Gusfield, Efficient methods for multiple sequence alignment with guaranteed error bounds, Bulletin of Mathematical Biology 55, pp. 141-154, 1993.
5. J. J. Hein, A new method that simultaneously aligns and reconstructs ancestral sequences for any number of homologous sequences, when the phylogeny is given, Mol. Biol. Evol. 6(6), pp. 649-668, 1989.
6. D.J. Lipman, S. F. Altschul, and J. D. Kececioglu, A tool for multiple sequence alignment. Proc. Natl. Acad. Sci. USA., 86:4412-4415, 1989.
7. R. Ravi and J. Kececioglu, Approximation algorithms for multiple sequence alignment under a fixed evolutionary tree, CPM95, pp. 330-339.
8. D. Sankoff, Minimal mutation trees of sequences, SIAM J. Applied Math. 28(1), pp. 35-42, 1975.
9. D. Sankoff, R. J. Cedergren and G. Lapalme, Frequency of insertion-deletion, transversion, and transition in the evolution of 5S ribosomal RNA, J. Mol. Evol. 7, pp. 133-149, 1976.
10. D. Sankoff and R. Cedergren, Simultaneous comparisons of three or more sequences related by a tree, In D. Sankoff and J. Kruskal, editors, Time warps, string edits, and macromolecules: the theory and practice of sequence comparison, pp. 253-264, Addison Wesley, 1983.
11. D. Sankoff and J. Kruskal, Time warps, string edits, and macromolecules: the theory and practice of sequence comparison, Addison Wesley, 1983
12. R. Ravi and J. Kececioglu, Approximation algorithms for multiple sequence alignment under a fixed evolutionary tree, CPM95, pp. 330-339.
13. M.S. Waterman and M.D. Perlwitz, Line geometries for sequence comparisons”, Bull. Math. Biol. 46, pp. 567-577, 1984.
14. L. Wang and T. Jiang, On the complexity of multiple sequence alignment, Journal of Computational Biology, vol. 1, pp. 337-348, 1994.
15. L. Wang, T. Jiang, and E.L. Lawler, Aligning sequences via an evolutionary tree: complexity and approximation, Algorithmica, to appear; also presented at the 26th ACM Symp. on Theory of Computing, 1994.