Weighted ASTRID: fast and accurate species trees from weighted internode distances

Abstract

Background 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. Results Our experimental study evaluating weighted ASTRID typically shows improvements in accuracy compared to the original (unweighted) ASTRID, and shows competitive accuracy against weighted ASTRAL, the state of the art. Our re-implementation of ASTRID also improves the runtime, with marked improvements on large datasets. Conclusions Weighted ASTRID is a new and very fast method for species tree estimation that typically improves upon ASTRID and has comparable accuracy to weighted ASTRAL, while remaining much faster. Weighted ASTRID is available at https://github.com/RuneBlaze/internode.

Testing the mettle of METAL: A comparison of phylogenomic methods using a challenging but well-resolved phylogeny

“…After all, the published ML concatenation topologies were known to include most reliable clades and it was very unlikely that our reanalyses using ML concatenation would yield trees that differed from those in Jarvis et al (2014) in fundamental ways. The Jarvis datasets were also used in the papers describing wASTRAL and wASTRID (Zhang and Mirarab, 2022; Liu and Warnow, 2023), so there was already evidence that those methods can recover many reliable clades (although neither of those studies analyzed UCEs or introns separately). Thus, the primary questions we could ask using the Jarvis datasets was whether SVDquartets and/or METAL would exhibit good performance.…”

“…We also used IQ-TREE to generate ML estimates of gene trees for summary coalescent analyses; as above, we used the -m TEST option to identify the best-fitting model and we used the parametric approximate likelihood ratio test (the -alrt 0 option) (Anisimova and Gascuel, 2006) to assess branch support in the gene trees. The gene trees with support values were used as input for the weighted versions of ASTRAL (wASTRAL, Zhang and Mirarab, 2022) and ASTRID (wASTRID, Liu and Warnow, 2023). We assessed support in wASTRAL using the local posterior probabilities (Sayyari and Mirarab, 2016) generated by that program.…”