ArticleJournal of mathematical biology2022
Identifiability of species network topologies from genomic sequences using the logDet distance.
Article in Journal of mathematical biology, 2022. 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, 21 citations in OpenAlex.
- Article
- 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
- Identifiability of Level-1 Species Networks from Gene Tree Quartets.Bulletin of mathematical biology · 2024Article
- Article
- Exploring spaces of semi-directed level-1 networks.Journal of mathematical biology · 2023Article
- Classes of explicit phylogenetic networks and their biological and mathematical significance.Journal of mathematical biology · 2022Review
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Inference of network-like evolutionary relationships between species from genomic data must address the interwoven signals from both gene flow and incomplete lineage sorting. The heavy computational demands of standard approaches to this problem severely limit the size of datasets that may be analyzed, in both the number of species and the number of genetic loci. Here we provide a theoretical pointer to more efficient methods, by showing that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case. This result is obtained under the Network Multispecies Coalescent model combined with a mixture of General Time-Reversible sequence evolution models across individual gene trees. It applies to both unlinked site data, such as for SNPs, and to sequence data in which many contiguous sites may have evolved on a common tree, such as concatenated gene sequences. Thus under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees.
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