Fast and Accurate Species Trees from Weighted Internode Distances

Abstract

Species tree estimation is a basic step in many biological research projects, but is complicated by the fact that gene trees can differ from the species tree due to processes such as incomplete lineage sorting (ILS), gene duplication and loss (GDL), and horizontal gene transfer (HGT), which can cause different regions within the genome to have different evolutionary histories (i.e., “gene tree heterogeneity”). One approach to estimating species trees in the presence of gene tree heterogeneity resulting from ILS operates by computing trees on each genomic region (i.e., computing “gene trees”) and then using these gene trees to define a matrix of average internode distances, where the internode distance in a tree T between two species x and y is the number of nodes in T between the leaves corresponding to x and y. Given such a matrix, a tree can then be computed using methods such as neighbor joining. Methods such as ASTRID and NJst (which use this basic approach) are provably statistically consistent, very fast (low degree polynomial time) and have had high accuracy under many conditions that makes them competitive with other popular species tree estimation methods. In this study, inspired by the very recent work of weighted ASTRAL, we present weighted ASTRID, a variant of ASTRID that takes the branch uncertainty on the gene trees into account in the internode distance. Our experimental study evaluating weighted ASTRID shows improvements in accuracy compared to the original (unweighted) ASTRID while remaining fast. Moreover, weighted ASTRID shows competitive accuracy against weighted ASTRAL, the state of the art. Thus, this study provides a new and very fast method for species tree estimation that improves upon ASTRID, has comparable accuracy with the state of the art while remaining much faster. Weighted ASTRID is available at https://github.com/RuneBlaze/internode.

Integrating Phylogenies with Chronology to Assemble the Tree of Life

“…Relative performance of many different versions of the imputation and quartet puzzling approaches have been examined for gene tree reconciliation with and without missing data (Liu and Warnow, 2023;Zhang and Mirarab, 2022;Rabiee et al, 2019;Zhang et al, 2020;Cao and Nakhleh, 2019). The general conclusion seems to be that they perform well and similarly.…”