## Performance of supertree methods on various dataset decompositions (2004)

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Venue: | Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life, volume 3 of Computational Biology |

Citations: | 17 - 9 self |

### BibTeX

@INPROCEEDINGS{Roshan04performanceof,

author = {Usman Roshan and Bernard M. E and Moret Tiffani and L. Williams and Tandy Warnow},

title = {Performance of supertree methods on various dataset decompositions},

booktitle = {Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life, volume 3 of Computational Biology},

year = {2004},

pages = {301--328},

publisher = {Kluwer Academic Publishers}

}

### OpenURL

### Abstract

Many large-scale phylogenetic reconstruction methods attempt to solve hard optimization problems (such as Maximum Parsimony (MP) and Maximum Likelihood (ML)), but they are severely limited by the number of taxa that they can handle in a reasonable time frame. A standard heuristic approach to this problem is the divide-and-conquer strategy: decompose the dataset into smaller subsets, solve the subsets (i.e., use MP or ML on each subset to obtain trees), then combine the solutions to the subsets into a solution to the original dataset. This last step, combining given trees into a single tree, is known as supertree construction in computational phylogenetics. The traditional application of supertree methods is to combine existing, published phylogenies into a single phylogeny. Here, we study supertree construction in the context of divide-and-conquer methods for large-scale tree reconstruction. We study several divide-and-conquer approaches and experimentally demonstrate their advantage over Matrix Representation Parsimony (MRP), a traditional supertree technique, and over global heuristics such as the parsimony ratchet. On the ten large biological datasets under investigation, our study shows that the techniques used for dividing the dataset into subproblems as well as those used for merging them into a single solution strongly influence the quality of the supertree construction. In most cases, our merging technique—the Strict Consensus Merger (SCM)—outperforms MRP with respect to MP scores and running time. Divideand-conquer techniques are also a highly competitive alternative to global heuristics such as the parsimony ratchet, especially on the more challenging datasets. 1