title: Iterative pass optimization of sequence data.
abstract: The problem of determining the minimum-cost hypothetical ancestral sequences for a given cladogram is known to be NP-complete. This "tree alignment" problem has motivated the considerable effort placed in multiple sequence alignment procedures. Wheeler in 1996 proposed a heuristic method, direct optimization, to calculate cladogram costs without the intervention of multiple sequence alignment. This method, though more efficient in time and more effective in cladogram length than many alignment-based procedures, greedily optimizes nodes based on descendent information only. In their proposal of an exact multiple alignment solution, Sankoff et al. in 1976 described a heuristic procedure--the iterative improvement method--to create alignments at internal nodes by solving a series of median problems. The combination of a three-sequence direct optimization with iterative improvement and a branch-length-based cladogram cost procedure, provides an algorithm that frequently results in superior (i.e., lower) cladogram costs. This iterative pass optimization is both computation and memory intensive, but economies can be made to reduce this burden. An example in arthropod systematics is discussed.
authors.affiliation: Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024-5192, USA. wheeler@amnh.org
authors.forename: Ward C
authors.initials: WC
authors.lastname: Wheeler
mesh_terms: D000465:Algorithms; D000818:Animals; D001181:Arthropods / Q000145:classification* / Q000235:genetics; D005075:Biological Evolution; D008969:Molecular Sequence Data; D010802:Phylogeny; D016415:Sequence Alignment / Q000379:methods*; D017385:Sequence Homology