ArticleBioinformatics (Oxford, England)2019
TreeMerge: a new method for improving the scalability of species tree estimation methods.
Article in Bioinformatics (Oxford, England), 2019. 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.
- Practical phylogenetic usage of theoretical advances in distance-based tree learning.BMC bioinformatics · 2026Article
- Unraveling the Ancient Introgression History ofInternational journal of molecular sciences · 2025Article
- Efficient phylogenetic tree inference for massive taxonomic datasets: harnessing the power of a server to analyze 1 million taxa.GigaScience · 2024Article
- Fusang: a framework for phylogenetic tree inference via deep learning.Nucleic acids research · 2023Article
- Spectral top-down recovery of latent tree models.Information and inference : a journal of the IMA · 2023Article
- Recent progress on methods for estimating and updating large phylogenies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Phylogeny Estimation Given Sequence Length Heterogeneity.Systematic biology · 2021Article
- Novel metric for hyperbolic phylogenetic tree embeddings.Biology methods & protocols · 2021Article
- Multiple Sequence Alignment for Large Heterogeneous Datasets Using SATé, PASTA, and UPP.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Unblended disjoint tree merging using GTM improves species tree estimation.BMC genomics · 2020Article
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2 authors.
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Abstract
motivationAt RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species.
resultsHere we present a new method called 'TreeMerge' that improves on NJMerge in two ways: it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework-only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. AVAILABILITY AND IMPLEMENTATION: TreeMerge is publicly available on Github (http://github.com/ekmolloy/treemerge). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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