Enhancing Searches for Optimal Trees Using SIESTA
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Many supertree estimation and multi-locus species tree estimation methods compute trees by combining trees on subsets of the species set based on some NP-hard optimization criterion. A recent approach to computing large trees has been to constrain the search space by defining a set of “allowed bipartitions”, and then use dynamic programming to find provably optimal solutions in polynomial time. Several phylogenomic estimation methods, such as ASTRAL, the MDC algorithm in PhyloNet, and FastRFS, use this approach. We present SIESTA, a method that allows the dynamic programming method to return a data structure that compactly represents all the optimal trees in the search space. As a result, SIESTA provides multiple capabilities, including: (1) counting the number of optimal trees, (2) calculating consensus trees, (3) generating a random optimal tree, and (4) annotating branches in a given optimal tree by the proportion of optimal trees it appears in. SIESTA is available in open source form on github at https://github.com/pranjalv123/SIESTA.
We thank the anonymous reviewers for their helpful criticisms on an earlier draft, which greatly improved the manuscript. We also thank Erin Molloy, Sarah Christensen, and Siavash Mirarab, for feedback on the initial results.
Funding. This study made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program in conjunction with the National Center for Supercomputing Applications and which is supported by funds from the University of Illinois at Urbana-Champaign. This work was partially supported by U.S. National Science Foundation Graduate Research Fellowship Program under Grant Number DGE-1144245 to PV and U.S. National Science Foundation grant CCF-1535977 to TW.
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