Abstract
The gene-duplication problem is to infer a species supertree from a collection of gene trees that are confounded by complex histories of gene duplications. This problem is NP-hard and thus requires efficient and effective heuristics. Existing heuristics perform a stepwise search of the tree space, where each step is guided by an exact solution to an instance of a local search problem. We show how this local search problem can be solved efficiently by reusing previously computed information. This improves the running time of the current solution by a factor of n, where n is the number of species in the resulting supertree solution, and makes the gene-duplication problem more tractable for large-scale phylogenetic analyses. We verify the exceptional performance of our solution in a comparison study using sets of large randomly generated gene trees. Furthermore, we demonstrate the utility of our solution by incorporating large genomic data sets from GenBank into a supertree analysis of plants.
During this research, O.E. and M.S.B. were supported in part by NSF grant no. 0334832 and J.G.B. by NESCent NSF grant no. EF-0423641.
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Bansal, M.S., Burleigh, J.G., Eulenstein, O., Wehe, A. (2007). Heuristics for the Gene-Duplication Problem: A Θ(n) Speed-Up for the Local Search. In: Speed, T., Huang, H. (eds) Research in Computational Molecular Biology. RECOMB 2007. Lecture Notes in Computer Science(), vol 4453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71681-5_17
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