Theory and practice of parallel direct optimization

Our ability to collect and distribute genomic and other biological data is growing at a staggering rate (Pagel, 1999). However, the synthesis of these data into knowledge of evolution is incomplete. Phylogenetic systematics provides a unifying intellectual approach to understanding evolution but presents formidable computational challenges. A fundamental goal of systematics, the generation of evolutionary trees, is typically approached as two distinct NP-complete problems: multiple sequence alignment and phylogenetic tree search. The number of cells in a multiple alignment matrix are exponentially related to sequence length. In addition, the number of evolutionary trees expands combinatorially with respect to the number of organisms or sequences to be examined. Biologically interesting datasets are currently comprised of hundreds of taxa and thousands of nucleotides and morphological characters. This standard will continue to grow with the advent of highly automated sequencing and development of character databases. Three areas of innovation are changing how evolutionary computation can be addressed: (1) novel concepts for determination of sequence homology, (2) heuristics and shortcuts in tree-search algorithms, and (3) parallel computing. In this paper and the online software documentation we describe the basic usage of parallel direct optimization as implemented in the software POY (ftp://ftp.amnh.org/pub/molecular/poy).