Data from: ASTRAL: genome-scale coalescent-based species tree estimation

Species trees provide insight into basic biology, including the mechanisms
of evolution and how it modifies biomolecular function and structure,
biodiversity and co-evolution between genes and species. Yet, gene trees
often differ from species trees, creating challenges to species tree
estimation. One of the most frequent causes for conflicting topologies
between gene trees and species trees is incomplete lineage sorting (ILS),
which is modelled by the multi-species coalescent. While many methods have
been developed to estimate species trees from multiple genes, some which
have statistical guarantees under the multi-species coalescent model,
existing methods are too computationally intensive for use with
genome-scale analyses or have been shown to have poor accuracy under some
realistic conditions. Results: We present ASTRAL, a fast method for
estimating species trees from multiple genes. ASTRAL is statistically
consistent, can run on datasets with thousands of genes and has
outstanding accuracy—improving on MP-EST and the population tree from
BUCKy, two statistically consistent leading coalescent-based methods.
ASTRAL is often more accurate than concatenation using maximum likelihood,
except when ILS levels are low or there are too few gene trees.