ArticleBMC genomics2020
STELAR: a statistically consistent coalescent-based species tree estimation method by maximizing triplet consistency.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Triplet-based species tree estimation: Sensitivity to gene tree rooting (or lack thereof).PLoS computational biology · 2026Article
- Leveraging Weighted Quartet Distributions for Enhanced Species Tree Inference from Genome-Wide Data.Genome biology and evolution · 2025Article
- wQFM-TREE: highly accurate and scalable quartet-based species tree inference from gene trees.Bioinformatics advances · 2025Article
- Terraces in species tree inference from gene trees.BMC ecology and evolution · 2024Article
- Dollo-CDP: a polynomial-time algorithm for the clade-constrained large Dollo parsimony problem.Algorithms for molecular biology : AMB · 2024Article
- OrthoMaM v12: a database of curated single-copy ortholog alignments and trees to study mammalian evolutionary genomics.Nucleic acids research · 2024Article
- wQFM-DISCO: DISCO-enabled wQFM improves phylogenomic analyses despite the presence of paralogs.Bioinformatics advances · 2024Article
- Quartets enable statistically consistent estimation of cell lineage trees under an unbiased error and missingness model.Algorithms for molecular biology : AMB · 2023Article
- Article
- Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices.BMC genomics · 2020Article
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5 authors.
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Abstract
backgroundSpecies tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, estimating a species tree from a collection of gene trees can be complicated due to the presence of gene tree incongruence resulting from incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent process. Maximum likelihood and Bayesian MCMC methods can potentially result in accurate trees, but they do not scale well to large datasets.
resultsWe present STELAR (Species Tree Estimation by maximizing tripLet AgReement), a new fast and highly accurate statistically consistent coalescent-based method for estimating species trees from a collection of gene trees. We formalized the constrained triplet consensus (CTC) problem and showed that the solution to the CTC problem is a statistically consistent estimate of the species tree under the multi-species coalescent (MSC) model. STELAR is an efficient dynamic programming based solution to the CTC problem which is highly accurate and scalable. We evaluated the accuracy of STELAR in comparison with SuperTriplets, which is an alternate fast and highly accurate triplet-based supertree method, and with MP-EST and ASTRAL - two of the most popular and accurate coalescent-based methods. Experimental results suggest that STELAR matches the accuracy of ASTRAL and improves on MP-EST and SuperTriplets.
conclusionsTheoretical and empirical results (on both simulated and real biological datasets) suggest that STELAR is a valuable technique for species tree estimation from gene tree distributions.
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