Issue No. 06 - Nov.-Dec. (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.162
Andre Wehe , Dept. of Biol., Univ. of Florida, Gainesville, FL, USA
J. Gordon Burleigh , Dept. of Comput. Sci., Iowa State Univ., Ames, IA, USA
Oliver Eulenstein , Dept. of Comput. Sci., Iowa State Univ., Ames, IA, USA
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.
Phylogeny, Algorithm design and analysis, Heuristic algorithms, Bioinformatics, Radio frequency, Computational biology, Search problems,supermatrix, Phylogenetics, supertree
Andre Wehe, J. Gordon Burleigh, Oliver Eulenstein, "Efficient Algorithms for Knowledge-Enhanced Supertree and Supermatrix Phylogenetic Problems", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. , pp. 1432-1441, Nov.-Dec. 2013, doi:10.1109/TCBB.2012.162