FASTRAL: improving scalability of phylogenomic analysis

Abstract

Motivation ASTRAL is the current leading method for species tree estimation from phylogenomic datasets (i.e., hundreds to thousands of genes) that addresses gene tree discord resulting from incomplete lineage sorting (ILS). ASTRAL is statistically consistent under the multi-locus coalescent model (MSC), runs in polynomial time, and is able to run on large datasets. Key to ASTRAL’s algorithm is the use of dynamic programming to find an optimal solution to the MQSST (maximum quartet support supertree) within a constraint space that it computes from the input. Yet, ASTRAL can fail to complete within reasonable timeframes on large datasets with many genes and species, because in these cases the constraint space it computes is too large. Results Here we introduce FASTRAL, a phylogenomic estimation method. FASTRAL is based on ASTRAL, but uses a different technique for constructing the constraint space. The technique we use to define the constraint space maintains statistical consistency and is polynomial time; thus we prove that FASTRAL is a polynomial time algorithm that is statistically consistent under the MSC. Our performance study on both biological and simulated data sets demonstrates that FASTRAL matches or improves on ASTRAL with respect to species tree topology accuracy (and under high ILS conditions it is statistically significantly more accurate), while being dramatically faster—especially on datasets with large numbers of genes and high ILS—due to using a significantly smaller constraint space. Availability FASTRAL is available in open-source form at https://github.com/PayamDiba/FASTRAL. Supplementary information Supplementary data are available at Bioinformatics online.

Phylogenomic resolution of the root of Panpulmonata, a hyperdiverse radiation of gastropods: new insight into the evolution of air breathing

“…Evolutionary relationships were also evaluated under the multi-species coalescent (MSC) using ASTRAL [61] and FAS-TRAL [62], which infer species trees from gene trees in contrast with concatenation approaches. Nodal support was assessed by bootstrapping (ASTRAL) or local Pp (both methods).…”

<strong></strong> Recent Progress on Methods for Estimating and Updating Large Phylogenies

“…The bipartitions from those estimated trees are then used as the constraint set for ASTRAL. This approach, which is called "FASTRAL" (Dibaeinia et al, 2021), is provably statistically consistent under the multi-species coalescent model, and comparisons on simulated and biological datasets reported in Dibaeinia et al (2021) show FASTRAL generally is similar in accuracy to ASTRAL while being much faster when the number of species and/or genes is large enough. Finally, FASTRAL-J, a combination of FASTRAL and ASTRAL-J, has been developed that provides runtime advantages over ASTRAL-J and comparable accuracy (Liu and Warnow, 2021).…”

“…However, FASTRAL has the potential to reduce accuracy if the ASTRID trees constrain the search space too much, removing true bipartitions compared to ASTRAL's default search space. While substantial reductions in accuracy were not observed in Dibaeinia et al (2021), clearly more extensive explorations are needed, in order to understand the conditions under which FASTRAL can be safely used, without degrading accuracy. A similar statement is true for FASTRAL-J, which also uses FASTRAL to constrain the search space.…”

<strong></strong> Recent Progress on Methods for Estimating and Updating Large Phylogenies

“…The bipartitions from those estimated trees are then used as the constraint set for ASTRAL. This approach, which is called "FASTRAL" (Dibaeinia et al, 2021), is provably statistically consistent under the multi-species coalescent model, and much faster than ASTRAL. Interestingly, it is also competitive with ASTRAL for accuracy -and sometimes more accurate!…”