Abstract
BackgroundMany 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, FastRFS, and ALE, use this approach.ResultsWe present SIESTA, a method that can be combined with these dynamic programming algorithms 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.ConclusionsSIESTA improves the accuracy of FastRFS and ASTRAL, and is a general technique for enhancing dynamic programming methods for constrained optimization.
Highlights
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
We explore the impact of using SIESTA with two methods that use dynamic programming for constrained exact optimization: the supertree method FastRFS [6] and the incomplete lineage sorting (ILS)-aware species tree estimation method ASTRAL [13]
We show how to correct these support values to take the full set of optimal ASTRAL trees into account, and enable the calculation of a maximum clade credibility (MCC) tree based on these corrected values
Summary
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. Phylogeny estimation is generally approached as a statistical estimation problem, and finding the best tree for a given dataset is typically based on methods that are computationally very intensive; for example, maximum likelihood phylogeny estimation is NP-hard [1] and Bayesian MCMC methods require a long time to converge. For this reason, among others, the calculation of very large phylogenies is often enabled by divide-andconquer methods that use “supertree methods” to combine smaller trees into larger trees. Examples of such “summary methods” (i.e., methods that construct species trees by combining gene trees) that are statistically consistent under the multi-species coalescent model include ASTRAL [12,13,14], GLASS [15], the population tree in BUCKy [16], MP-EST [17], NJst [18], and a modification of NJst called ASTRID [19]
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