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Testing phylogenetic signal with categorical traits and tree uncertainty
Abstract
Summary: The phylogenetic signal, frequently used to identify signatures of adaptive evolution or important associations between genes and phenotypes, measures the tendency for recently diverged species to resemble each other more than distantly related species. An example of such a measure is the δ statistic, which uses Shannon entropy to measure the degree of phylogenetic signal between a categorical trait and a phylogeny. In this study, we refined this statistic to account for tree uncertainty, resulting in more accurate assessments of phylogenetic associations. In addition, we provided a more accessible and computationally efficient implementation of the δ statistic that will facilitate its use by the evolutionary community. Availability and implementation: github.com/diogo-s-ribeiro/delta-statistic.
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Computational accessibility can also be provided through publicly available web applications.
“Computational accessibility can also be provided through publicly available web applications (Ribeiro et al. 2023; Dong et al. 2024; Morel et al. 2024).”
From From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions (2025) · cites this paper as Ribeiro et al. 2023
The delta statistic calculates phylogenetic signal for categorical traits.
“Ribeiro and Borges presented the delta statistic for calculating phylogenetic signal for categorical traits (Borges et al. 2019; Ribeiro et al. 2023).”
From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Ribeiro et al. 2023
The delta statistic does not use evolutionary rates.
“the delta statistic does not use evolutionary rates”
From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Ribeiro et al. 2023
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Research works printed but not linked: 7
- Blomberg, 2002, Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods, J Evol Biol, 15, 899, 10.1046/j.1420-9101.2002.00472.x · DOI
- Borges, 2019, Measuring phylogenetic signal between categorical traits and phylogenies, Bioinformatics, 35, 1862, 10.1093/bioinformatics/bty800 · DOI · Paper page
- Field, 2020, The evolution of parental care strategies in subsocial wasps, Behav Ecol Sociobiol, 74, 1, 10.1007/s00265-020-02853-w · DOI
- Höhna, 2016, RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language, Syst Biol, 65, 726, 10.1093/sysbio/syw021 · DOI
- Ishikawa, 2019, A fast likelihood method to reconstruct and visualize ancestral scenarios, Mol Biol Evol, 36, 2069, 10.1093/molbev/msz131 · DOI
- Scornavacca, 2019, Orthomam v10: scaling-up orthologous coding sequence and exon alignments with more than one hundred mammalian genomes, Mol Biol Evol, 36, 861, 10.1093/molbev/msz015 · DOI
- Yahnke, 2013, Animal diversity web as a teaching & learning tool to improve research & writing skills in college biology courses, Am Biol Teacher, 75, 494, 10.1525/abt.2013.75.7.9 · DOI
Cited by
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Redlich, R., Kowalczyk, A., Tene, M., Sestili, H. H., Foley, K., Saputra, E., Clark, N., Chikina, M., Meyer, W. K., & Pfenning, A. R. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae210 · Cited by 14
From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions
Macdonald, A. R., James, M. E., Mitchell, J. D., & Holland, B. R. (2025) · Genome Biology and Evolution · doi:10.1093/gbe/evaf150 · Cited by 5
Metadata
- Authors
- Ribeiro, D., Borges, R., Rocha, A. P., & Antunes, A.
- Affiliations
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões , 4450-208 Porto, Portugal
- Institut für Populationsgenetik, Vetmeduni Vienna , 1210 Vienna, Austria
- CMUP, Centre of Mathematics of the University of Porto , 4169-007 Porto, Portugal
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-07-01
- Volume
- 39
- Issue
- 7
- ISSN
- 1367-4811
- PMC
- PMC10374490
- Cited by
- 9
- References
- 0
- Language
- en
- Funders
- Austrian Science Fund (P34524-B)
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