Comparing best-first search and dynamic programming for optimal multiple sequence alignment

Authors:
Heath Hohwald;Ignacio Thayer;Richard E. Korf
Affiliations:
Computer Science Department, University of California, Los Angeles, Los Angeles, CA;Computer Science Department, University of California, Los Angeles, Los Angeles, CA;Computer Science Department, University of California, Los Angeles, Los Angeles, CA
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

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Abstract

Sequence alignment is an important problem in computational biology. We compare two different approaches to the problem of optimally aligning two or more character strings: bounded dynamic programming (BDP), and divide-and-conquer frontier search (DCFS). The approaches are compared in terms of time and space requirements in 2 through 5 dimensions with sequences of varying similarity and length. While BDP performs better in two and three dimensions, it consumes more time and memory than DCFS for higher-dimensional problems.