title: ASTRID: Accurate Species TRees from Internode Distances.
abstract: BACKGROUND
  Incomplete lineage sorting (ILS), modelled by the multi-species coalescent (MSC), is known to create discordance between gene trees and species trees, and lead to inaccurate species tree estimations unless appropriate methods are used to estimate the species tree. While many statistically consistent methods have been developed to estimate the species tree in the presence of ILS, only ASTRAL-2 and NJst have been shown to have good accuracy on large datasets. Yet, NJst is generally slower and less accurate than ASTRAL-2, and cannot run on some datasets.
  
  
  RESULTS
  We have redesigned NJst to enable it to run on all datasets, and we have expanded its design space so that it can be used with different distance-based tree estimation methods. The resultant method, ASTRID, is statistically consistent under the MSC model, and has accuracy that is competitive with ASTRAL-2. Furthermore, ASTRID is much faster than ASTRAL-2, completing in minutes on some datasets for which ASTRAL-2 used hours.
  
  
  CONCLUSIONS
  ASTRID is a new coalescent-based method for species tree estimation that is competitive with the best current method in terms of accuracy, while being much faster. ASTRID is available in open source form on github.
authors.forename: Pranjal
authors.initials: P
authors.lastname: Vachaspati
authors.forename: Tandy
authors.initials: T
authors.lastname: Warnow
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D019143:Evolution, Molecular; D049810:Genetic Speciation; D023281:Genomics; D008962:Models, Theoretical; D010802:Phylogeny