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    MRL and SuperFine+MRL: new supertree methods

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    1748-7188-7-3.pdf (387.8Kb)
    Date
    2012-01-26
    Author
    Nguyen, Nam
    Mirarab, Siavash
    Warnow, Tandy
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    Abstract
    Background: Supertree methods combine trees on subsets of the full taxon set together to produce a tree on the entire set of taxa. Of the many supertree methods, the most popular is MRP (Matrix Representation with Parsimony), a method that operates by first encoding the input set of source trees by a large matrix (the "MRP matrix") over {0,1, ?}, and then running maximum parsimony heuristics on the MRP matrix. Experimental studies evaluating MRP in comparison to other supertree methods have established that for large datasets, MRP generally produces trees of equal or greater accuracy than other methods, and can run on larger datasets. A recent development in supertree methods is SuperFine+MRP, a method that combines MRP with a divide-and-conquer approach, and produces more accurate trees in less time than MRP. In this paper we consider a new approach for supertree estimation, called MRL (Matrix Representation with Likelihood). MRL begins with the same MRP matrix, but then analyzes the MRP matrix using heuristics (such as RAxML) for 2-state Maximum Likelihood. Results: We compared MRP and SuperFine+MRP with MRL and SuperFine+MRL on simulated and biological datasets. We examined the MRP and MRL scores of each method on a wide range of datasets, as well as the resulting topological accuracy of the trees. Our experimental results show that MRL, coupled with a very good ML heuristic such as RAxML, produced more accurate trees than MRP, and MRL scores were more strongly correlated with topological accuracy than MRP scores. Conclusions: SuperFine+MRP, when based upon a good MP heuristic, such as TNT, produces among the best scores for both MRP and MRL, and is generally faster and more topologically accurate than other supertree methods we tested.
    Department
    Computer Sciences
    Description
    Nam Nguyen, Siavash Mirarab and Tandy Warnow are with the Department of Computer Science, University of Texas at Austin, Austin, Texas, USA
    Subject
    MRP
    MRL
    supertrees
    phylogenetics
    URI
    http://hdl.handle.net/2152/27792
    xmlui.dri2xhtml.METS-1.0.item-citation
    Nguyen, Nam, Siavash Mirarab, and Tandy Warnow. “MRL and SuperFine+MRL: New Supertree Methods.” Algorithms for Molecular Biology 7, no. 1 (January 26, 2012): 3. doi:10.1186/1748-7188-7-3.
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