SIESTA: enhancing searches for optimal supertrees and species trees.

ABSTRACT:

Background

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, FastRFS, and ALE, use this approach.

Results

We 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, incl

SUBMITTER: Vachaspati P 

PROVIDER: S-EPMC5998881 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature