An ILP solution for the gene duplication problem

BMC Bioinformatics

Research

Gordon J Burleigh¹  David F Fernández-Baca²  Wen-Chieh Chang²  Oliver Eulenstein² 

[1] Department of Biology, University of Florida, 32611, Gainesville, USA;Department of Computer Science, Iowa State University, 50011, Ames, USA;

关键词: Species Tree;    Gene Tree;    Integer Linear Programming;    Integer Linear Programming Formulation;    Deep Coalescence;   

DOI  :  10.1186/1471-2105-12-S1-S14

来源: Springer

【 摘 要 】

BackgroundThe gene duplication (GD) problem seeks a species tree that implies the fewest gene duplication events across a given collection of gene trees. Solving this problem makes it possible to use large gene families with complex histories of duplication and loss to infer phylogenetic trees. However, the GD problem is NP-hard, and therefore, most analyses use heuristics that lack any performance guarantee.ResultsWe describe the first integer linear programming (ILP) formulation to solve instances of the gene duplication problem exactly. With simulations, we demonstrate that the ILP solution can solve problem instances with up to 14 taxa. Furthermore, we apply the new ILP solution to solve the gene duplication problem for the seed plant phylogeny using a 12-taxon, 6, 084-gene data set. The unique, optimal solution, which places Gnetales sister to the conifers, represents a new, large-scale genomic perspective on one of the most puzzling questions in plant systematics.ConclusionsAlthough the GD problem is NP-hard, our novel ILP solution for it can solve instances with data sets consisting of as many as 14 taxa and 1, 000 genes in a few hours. These are the largest instances that have been solved to optimally to date. Thus, this work can provide large-scale genomic perspectives on phylogenetic questions that previously could only be addressed by heuristic estimates.

【 授权许可】

Unknown   
© Chang et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.