Memory-efficient A* heuristics for multiple sequence alignment

  • Authors:

  • Matthew McNaughton;Paul Lu;Jonathan Schaeffer;Duane Szafron

  • Affiliations:

  • Department of Computing Science, University of Alberta, Edmonton, Alberta Canada T6G 2E8;Department of Computing Science, University of Alberta, Edmonton, Alberta Canada T6G 2E8;Department of Computing Science, University of Alberta, Edmonton, Alberta Canada T6G 2E8;Department of Computing Science, University of Alberta, Edmonton, Alberta Canada T6G 2E8

  • Venue:
  • Eighteenth national conference on Artificial intelligence
  • Year:
  • 2002

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Abstract

The time and space needs of an A* search are strongly influenced by the quality of the heuristic evaluation function. Usually there is a trade-off since better heuristics may require more time and/or space to evaluate. Multiple sequence alignment is an important application for single-agent search. The traditional heuristic uses multiple pairwise alignments that require relatively little space. Three-way alignments produce better heuristics, but they are not used in practice due to the large space requirements. This paper presents a memory-efficient way to represent three-way heuristics as an octree. The required portions of the octree are computed on demand. The octree-supported three-way heuristics result in such a substantial reduction to the size of the A* open list that they offset the additional space and time requirements for the three-way alignments. The resulting multiple sequence alignments are both faster and use less memory than using A* with traditional pairwise heuristics.