Data from: Leveraging weighted quartet distributions for enhanced species tree inference from genome-wide data

Species tree estimation from genes sampled from throughout the whole
genome is challenging because of gene tree discordance, often caused by
incomplete lineage sorting (ILS). Quartet-based summary methods for
estimating species trees from a collection of gene trees are becoming
popular due to their high accuracy and theoretical guarantees of
robustness to arbitrarily high amounts of ILS. ASTRAL, the most widely
used quartet-based method, aims to infer species trees by maximizing the
number of quartets in the gene trees consistent with the species tree. An
alternative approach is inferring quartets for all subsets of four species
and amalgamating them into a coherent species tree. While summary methods
can be sensitive to gene tree estimation error, quartet amalgamation
offers an advantage by potentially bypassing gene tree estimation.
However, greatly understudied is the choice of weighted quartet inference
method and downstream effects on species tree estimations under realistic
model conditions. In this study, we investigated a wide array of methods
for generating weighted quartets and critically assessed their impact on
species tree inference. Our study provides evidence that the careful
generation and amalgamation of weighted quartets, as implemented in
methods like wQFM, can lead to significantly more accurate trees than
popular methods like ASTRAL, especially in the face of gene tree
estimation errors.