Research Article: MRL and SuperFine+MRL: new supertree methods

Date Published: January 26, 2012

Publisher: BioMed Central

Author(s): Nam Nguyen, Siavash Mirarab, Tandy Warnow.

http://doi.org/10.1186/1748-7188-7-3

Abstract

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.

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.

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.

Partial Text

Because estimation of large trees is computationally challenging [1-3] and topological error tends to increase with the number of taxa [4-7], supertree methods (which estimate trees on full sets of taxa from sets of smaller trees) may be key to accurate estimations of the Tree of Life. Many supertree methods have been proposed: see [8] for an overview of early methods, and also [9-17]. Some of these (e.g., the Robinson-Foulds supertree approach in [9]) operate only on rooted source trees, while others (e.g., the Maximum Likelihood Supertree Method in [15]) are only theoretical (i.e., have not yet been implemented). Of the various methods that are implemented, MRP (Matrix Representation with Parsimony) [18,19] is by far the most frequently used. Furthermore, studies have shown that of these methods, only MRP produces highly accurate supertrees on datasets of unrooted source trees with large numbers of taxa [17,20].

Supertree estimation methods need to be both highly accurate and also reasonably fast, as otherwise they will not be useful in estimating large phylogenies. Our discussion thus addresses both running time and topological accuracy.

The authors declare that they have no competing interests.

TW designed the study; NN and SM developed the software; NN performed the study; TW, NN, and SM analyzed the data and wrote the paper. All authors read and approved the final manuscript.

 

Source:

http://doi.org/10.1186/1748-7188-7-3

 

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