title: Efficient algorithms for knowledge-enhanced supertree and supermatrix phylogenetic problems.
abstract: Phylogenetic inference is a computationally difficult problem, and constructing high-quality phylogenies that can build upon existing phylogenetic knowledge and synthesize insights from new data remains a major challenge. We introduce knowledge-enhanced phylogenetic problems for both supertree and supermatrix phylogenetic analyses. These problems seek an optimal phylogenetic tree that can only be assembled from a user-supplied set of, possibly incompatible, phylogenetic relationships. We describe exact polynomial time algorithms for the knowledge-enhanced versions of the NP-hard Robinson Foulds, gene duplication, duplication and loss, and deep coalescence supertree problems. Further, we demonstrate that our algorithms can rapidly improve upon results of local search heuristics for these problems. Finally, we introduce a knowledge-enhanced search heuristic that can be applied to any discrete character data set using the maximum parsimony (MP) phylogenetic problem. Although this approach is not guaranteed to find exact solutions, we show that it also can improve upon solutions from commonly used MP heuristics.
authors.affiliation: University of Florida, Gainesville and Iowa State University, Ames.
authors.forename: André
authors.initials: A
authors.lastname: Wehe
authors.affiliation: University of Florida, Gainesville.
authors.forename: J Gordon
authors.initials: JG
authors.lastname: Burleigh
authors.affiliation: Iowa State University, Ames.
authors.forename: Oliver
authors.initials: O
authors.lastname: Eulenstein
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D020440:Gene Duplication; D010802:Phylogeny; D012984:Software