ArticleSystematic biology2022
StarBeast3: Adaptive Parallelized Bayesian Inference under the Multispecies Coalescent.
Article in Systematic biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
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- Population genomics of yellow-eyed penguins uncovers subspecies divergence and candidate genes linked to respiratory distress syndrome.Nature ecology & evolution · 2026Article
- Integrative evidence reveals adaptive divergence and speciation in gentoo penguins.Communications biology · 2026Article
- Scaling up Bayesian population phylogenomics through virtual dimension reduction.Nature communications · 2026Article
- Assessing the Potential of Ancient Protein Sequences in the Study of Hominid Evolution.Genome biology and evolution · 2026Article
- Early hominin arrival in Southeast Asia triggered the evolution of major human malaria vectors.Scientific reports · 2026Article
- Phylotranscriptomics and genome-size evidence clarify the Taiwanese Cirsium japonicum complex and delimit C. brevicaule and allied East Asian thistles.BMC plant biology · 2026Article
- Impact of incomplete lineage sorting and natural selection on the phylogenetic and taxonomic uncertainties of Aspidistra in Taiwan.Botanical studies · 2025Article
- Phylogenomic timetree-calibrated speciation clocks for Caenorhabditis nematodes reveal slow but disproportionate accumulation of post-zygotic reproductive isolation.PLoS genetics · 2025Article
- Multilocus phylogeny and phylogeographical patterns suggest the need for a comprehensive revision of the systematics of Stelliferinae Sasaki, 1989 (Sciaenidae, Acanthuriformes).Scientific reports · 2025Article
- Evolution is coupled with branching across many granularities of life.Proceedings. Biological sciences · 2025Article
- Iguanas rafted more than 8,000 km from North America to Fiji.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Chromosome number variation and phylogenetic divergence of East Asian Cirsium sect. Onotrophe subsect. Nipponocirsium (Compositae), with a new species from Taiwan.Botanical studies · 2025Article
- Unraveling the Ancient Introgression History ofInternational journal of molecular sciences · 2025Article
- How to Validate a Bayesian Evolutionary Model.Systematic biology · 2025Article
- Phylogenetic Signal in Primate Tooth Enamel Proteins and its Relevance for Paleoproteomics.Genome biology and evolution · 2025Article
- Phylogenetic analysis suggests early divergence followed by convergent morphological evolution in thePhytoKeys · 2025Article
- Recombination and phylogenetic inference.Evolutionary journal of the Linnean Society · 2025Article
- Evolutionary history of the snooks: Phylogeny, biogeography and diversification of the genus Centropomus.PloS one · 2025Article
- Deep genetic divergences and few morphological changes support the cryptic speciation in Larimus breviceps (Sciaenidae, Acanthuriformes) from the western South Atlantic.Scientific reports · 2024Article
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3 authors.
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Abstract
As genomic sequence data become increasingly available, inferring the phylogeny of the species as that of concatenated genomic data can be enticing. However, this approach makes for a biased estimator of branch lengths and substitution rates and an inconsistent estimator of tree topology. Bayesian multispecies coalescent (MSC) methods address these issues. This is achieved by constraining a set of gene trees within a species tree and jointly inferring both under a Bayesian framework. However, this approach comes at the cost of increased computational demand. Here, we introduce StarBeast3-a software package for efficient Bayesian inference under the MSC model via Markov chain Monte Carlo. We gain efficiency by introducing cutting-edge proposal kernels and adaptive operators, and StarBeast3 is particularly efficient when a relaxed clock model is applied. Furthermore, gene-tree inference is parallelized, allowing the software to scale with the size of the problem. We validated our software and benchmarked its performance using three real and two synthetic data sets. Our results indicate that StarBeast3 is up to one-and-a-half orders of magnitude faster than StarBeast2, and therefore more than two orders faster than *BEAST, depending on the data set and on the parameter, and can achieve convergence on large data sets with hundreds of genes. StarBeast3 is open-source and is easy to set up with a friendly graphical user interface. [Adaptive; Bayesian inference; BEAST 2; effective population sizes; high performance; multispecies coalescent; parallelization; phylogenetics.].
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