ASTER: A Package for Large-Scale Phylogenomic Reconstructions

2025/07/16 by Chao Zhang, Rasmus Nielsen, Siavash Mirarab · 1 voice · 54 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Mathematics · #Advanced Spaceborne Thermal Emission and Reflection Radiometer #Artificial intelligence #Biology #Chromosomal and Genetic Variations #Combinatorics #Computational biology #Computational science #Computer network #Computer science #Database #Gene #Genetic diversity and population structure #Genetics #Genome #Genomics and Phylogenetic Studies #Geography #Inference #Mathematics #Network topology #Phylogenetic tree #Phylogenomics #R package #Remote sensing #Scalability #Tree (set theory)

paper · pdf · doi:10.1093/molbev/msaf172

published in Molecular Biology and Evolution 42(8) (Oxford University Press)

openalex publication_date 2025/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Many algorithms are available for inferring species trees from various input types while accounting for gene tree discordance. Several quartet-based species tree inference methods, collectively known as the ASTRAL family, are based on similar ideas and are in wide use. Here, we integrate all ASTRAL-like methods into a single package called ASTER, comprising several tools, each designed for a different input type: (i) ASTRAL for single-copy gene tree topologies, (ii) weighted ASTRAL (wASTRAL) for single-copy gene tees with branch length and/or support, (iii) ASTRAL-Pro for multi-copy gene tree topologies, (iv) CASTER for multiple sequence alignments, including genome alignments, and (v) WASTER for short-reads and assembled genomes. These tools collectively enhance the scalability, accuracy, and versatility of species tree inference.

Citations

• Complexity of avian evolution revealed by family-level genomes

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Discussions

• C. Zhang, R. Nielsen and S. Mirarab integrate all ASTRAL-like methods into a single package called ASTER, comprising several tools that collectively enhance the scalability, accuracy, and versatility [bsky, 31 points, 0 comments]

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