ArticleBMC genomics2018
SIESTA: enhancing searches for optimal supertrees and species trees.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Gentrius: Generating Trees Compatible With a Set of Unrooted Subtrees and its Application to Phylogenetic Terraces.Molecular biology and evolution · 2024Article
- Dollo-CDP: a polynomial-time algorithm for the clade-constrained large Dollo parsimony problem.Algorithms for molecular biology : AMB · 2024Article
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2 authors.
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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.
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