RESEARCH Open Access ASTRID: Accurate Species TRees from Internode Distances

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. Background Species tree estimation in the presence of gene tree incon-gruence is a major challenge for many biological analyses. Gene tree incongruence can result from a variety of pro-cesses, notably incomplete lineage sorting (ILS) [1], which is modelled by the multispecies coalescent (MSC) [2]. Concatenated maximum likelihood analyses is generally the most common method for species tree estimation from multiple loci, but can be statistically inconsistent, and even positively misleading, in some cases [3], thus converging to an incorrect tree with increasing amounts of sequence data. In recent years, a number of species tree estimation methods have been developed that are statistically consis-tent under the MSC, and so will converge in probability to the true species trees as the amount of data increases; see [4-6]. Methods that are statistically consistent under th