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1. Forcing external constraints on tree inference using ASTRAL

   https://doi.org/10.1186/s12864-020-6607-z  

   Rabiee, Maryam; Mirarab, Siavash (April 2020, BMC Genomics)
2. ASTRAL-Pro: Quartet-Based Species-Tree Inference despite Paralogy

   https://doi.org/10.1093/molbev/msaa139  

   Zhang, Chao; Scornavacca, Celine; Molloy, Erin K; Mirarab, Siavash (September 2020, Molecular Biology and Evolution)

   Thorne, Jeffrey (Ed.)

   Abstract Phylogenetic inference from genome-wide data (phylogenomics) has revolutionized the study of evolution because it enables accounting for discordance among evolutionary histories across the genome. To this end, summary methods have been developed to allow accurate and scalable inference of species trees from gene trees. However, most of these methods, including the widely used ASTRAL, can only handle single-copy gene trees and do not attempt to model gene duplication and gene loss. As a result, most phylogenomic studies have focused on single-copy genes and have discarded large parts of the data. Here, we first propose a measure of quartet similarity between single-copy and multicopy trees that accounts for orthology and paralogy. We then introduce a method called ASTRAL-Pro (ASTRAL for PaRalogs and Orthologs) to find the species tree that optimizes our quartet similarity measure using dynamic programing. By studying its performance on an extensive collection of simulated data sets and on real data sets, we show that ASTRAL-Pro is more accurate than alternative methods. 

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3. ASTRAL-Pro 2: ultrafast species tree reconstruction from multi-copy gene family trees

   https://doi.org/10.1093/bioinformatics/btac620  

   Zhang, Chao; Mirarab, Siavash; Schwartz, ed., Russell (September 2022, Bioinformatics)

   Abstract MotivationSpecies tree inference from multi-copy gene trees has long been a challenge in phylogenomics. The recent method ASTRAL-Pro has made strides by enabling multi-copy gene family trees as input and has been quickly adopted. Yet, its scalability, especially memory usage, needs to improve to accommodate the ever-growing dataset size. ResultsWe present ASTRAL-Pro 2, an ultrafast and memory efficient version of ASTRAL-Pro that adopts a placement-based optimization algorithm for significantly better scalability without sacrificing accuracy. Availability and implementationThe source code and binary files are publicly available at https://github.com/chaoszhang/ASTER; data are available at https://github.com/chaoszhang/A-Pro2_data. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Misspecification Strikes: ASTRAL can Mislead in the Presence of Hybridization, even for Non-Anomalous Scenarios

   https://doi.org/10.1093/molbev/msaf049  

   Dinh, Vu; Baños, Hector (March 2025, Molecular Biology and Evolution)

   Falush, Daniel (Ed.)

   ASTRAL is a powerful and widely used tool for species tree inference, known for its computational speed and robustness under incomplete lineage sorting. The method has often been used as an initial step in species network inference to provide a backbone tree structure upon which hybridization events are later added to such a tree via other methods. However, we show empirically and theoretically, that this methodology can yield flawed results. Specifically, we demonstrate that under the Network Multispecies Coalescent model – including non-anomalous scenarios – ASTRAL can produce a tree that does not correspond to any topology displayed by the true underlying network. This finding highlights the need for caution when using ASTRAL-based inferences in suspected hybridization cases. 

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