ArticleBMC bioinformatics2011
An ILP solution for the gene duplication problem.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Phylogenetic reconciliation.PLoS computational biology · 2022Article
- DeCoDe: degenerate codon design for complete protein-coding DNA libraries.Bioinformatics (Oxford, England) · 2020Article
- Phylogenomics with paralogs.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- The inference of gene trees with species trees.Systematic biology · 2015Review
- Efficient error correction algorithms for gene tree reconciliation based on duplication, duplication and loss, and deep coalescence.BMC bioinformatics · 2012Article
- iGTP: a software package for large-scale gene tree parsimony analysis.BMC bioinformatics · 2010Article
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4 authors.
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Abstract
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.
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