Data for: Weighting by gene tree uncertainty improves accuracy of quartet-based species trees

Phylogenomic analyses routinely estimate species trees using methods that
account for gene tree discordance. However, the most scalable species tree
inference methods, which summarize independently inferred gene trees to
obtain a species tree, are sensitive to hard-to-avoid errors introduced in
the gene tree estimation step. This dilemma has created much debate on the
merits of concatenation versus summary methods and practical obstacles to
using summary methods more widely and to the exclusion of concatenation.
The most successful attempt at making summary methods resilient to noisy
gene trees has been contracting low support branches from the gene trees.
Unfortunately, this approach requires arbitrary thresholds and poses new
challenges. Here, we introduce threshold-free weighting schemes for the
quartet-based species tree inference, the metric used in the popular
method ASTRAL. By reducing the impact of quartets with low support or long
terminal branches (or both), weighting provides stronger theoretical
guarantees and better empirical performance than the unweighted ASTRAL.
Our simulations show that weighting improves accuracy across many
conditions and reduces the gap with concatenation in conditions with low
gene tree discordance and high noise. On empirical data, weighting
improves congruence with concatenation and increases support. Together,
our results show that weighting, enabled by a new optimization algorithm
we introduce, improves the utility of summary methods and can reduce the
incongruence often observed across analytical pipelines.