ArticleBulletin of mathematical biology2024
Identifiability of Level-1 Species Networks from Gene Tree Quartets.
Article in Bulletin of mathematical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- BROOQS: Spectral Methods Resolve Level-1 Hybridization Cycles without Tests of Symmetry.bioRxiv : the preprint server for biology · 2026Article
- Phylogenetic Network Models as Graphical Models.Bulletin of mathematical biology · 2026Article
- Distinguishing Phylogenetic Level-2 Networks with Quartets and Inter-Taxon Quartet Distances.Bulletin of mathematical biology · 2025Article
- Beyond Level-1: Identifiability of a Class of Galled Tree-Child Networks.Bulletin of mathematical biology · 2025Article
- When are Quarnets Sufficient to Reconstruct Semi-directed Phylogenetic Networks?Bulletin of mathematical biology · 2025Article
- Phylogenetic networks empower biodiversity research.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- NANUQAlgorithms for molecular biology : AMB · 2025Article
- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
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Abstract
When hybridization or other forms of lateral gene transfer have occurred, evolutionary relationships of species are better represented by phylogenetic networks than by trees. While inference of such networks remains challenging, several recently proposed methods are based on quartet concordance factors-the probabilities that a tree relating a gene sampled from the species displays the possible 4-taxon relationships. Building on earlier results, we investigate what level-1 network features are identifiable from concordance factors under the network multispecies coalescent model. We obtain results on both topological features of the network, and numerical parameters, uncovering a number of failures of identifiability related to 3-cycles in the network. Addressing these identifiability issues is essential for designing statistically consistent inference methods.
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