Data from: Improved robustness to gene tree incompleteness, estimation errors, and systematic homology errors with weighted TREE-QMC

Summary methods are widely used to reconstruct species trees from gene
tres while accommodating discordance from incomplete lineage sorting;
however, it is increasingly recognized that their accuracy can be
negatively impacted by incomplete and/or error-ridden gene trees. To
address the latter, Zhang and Mirarab (2022) updated the popular summary
method ASTRAL so that it weights quartets based on gene tree branch
lengths and support values. The implementation of these weighting schemes
presented computational challenges, leading Zhang and Mirarab (2022) to
replace ASTRAL's original algorithm (i.e., computing an exact
solution within a constrained search space) in favor of search heuristics
based on phylogenetic placement. Here, we show that these weighting
schemes can be effectively leveraged within the Quartet Max Cut framework
of Snir and Rao (2010), introducing weighted TREE-QMC. The incorporation
of weighting schemes into TREE-QMC required only a small increase in time
complexity compared to the unweighted algorithm; fortunately, the increase
in runtime was also small, behaving more like a constant factor in our
simulation study. Moreover, weighted TREE-QMC was fast and highly
competitive with weighted ASTRAL, even outperforming it in terms of
species tree accuracy on some challenging simulation conditions, such as
large numbers of taxa. In reanalyzing two avian data sets, we found that
weighting quartets by gene tree branch lengths can improve robustness to
systematic homology errors and can be as effective as removing the
impacted taxa from individual gene trees or removing the impacted gene
trees entirely. Lastly, our study revealed that TREE-QMC was robust to
extreme rates of missing taxa, suggesting its utility as a supertree
method.