Data from: SuperFine: fast and accurate supertree estimation

Many research groups are estimating trees containing anywhere from a few
thousand to hundreds of thousands of species, towards the eventual goal of
the estimation of a Tree of Life, containing perhaps as many as several
million leaves. These phylogenetic estimations present enormous
computational challenges, and current computational methods are likely to
fail to run even on datasets in the low end of this range. One approach to
estimate a large species tree is to use phylogenetic estimation methods
(such as maximum likelihood) on a supermatrix produced by concatenating
multiple sequence alignments for a collection of markers; however, the
most accurate of these phylogenetic estimation methods are extremely
computationally intensive for datasets with more than a few thousand
sequences. Supertree methods, which assemble phylogenetic trees from a
collection of trees on subsets of the taxa, are important tools for
phylogeny estimation where phylogenetic analyses based upon maximum
likelihood are infeasible. In this paper, we introduce SuperFine, a
meta-method that utilizes a novel two-step procedure in order to improve
the accuracy and scalability of supertree methods. Our study, using both
simulated and empirical data, shows that SuperFine-boosted supertree
methods produce more accurate trees than standard supertree methods, and
run quickly on very datasets with thousands of sequences. Furthermore,
SuperFine-boosted MRP (Matrix Representation with Parsimony, the most well
known supertree method) approaches the accuracy of maximum likelihood
methods on supermatrix datasets under realistic conditions.