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Testing phylogenetic signal with categorical traits and tree uncertainty

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...

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Detalles Bibliográficos
Autores principales: Ribeiro, Diogo, Borges, Rui, Rocha, Ana Paula, Antunes, Agostinho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374490/
https://www.ncbi.nlm.nih.gov/pubmed/37490431
http://dx.doi.org/10.1093/bioinformatics/btad433
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author Ribeiro, Diogo
Borges, Rui
Rocha, Ana Paula
Antunes, Agostinho
author_facet Ribeiro, Diogo
Borges, Rui
Rocha, Ana Paula
Antunes, Agostinho
author_sort Ribeiro, Diogo
collection PubMed
description 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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spelling pubmed-103744902023-07-29 Testing phylogenetic signal with categorical traits and tree uncertainty Ribeiro, Diogo Borges, Rui Rocha, Ana Paula Antunes, Agostinho Bioinformatics Applications Note 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. Oxford University Press 2023-07-25 /pmc/articles/PMC10374490/ /pubmed/37490431 http://dx.doi.org/10.1093/bioinformatics/btad433 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Ribeiro, Diogo
Borges, Rui
Rocha, Ana Paula
Antunes, Agostinho
Testing phylogenetic signal with categorical traits and tree uncertainty
title Testing phylogenetic signal with categorical traits and tree uncertainty
title_full Testing phylogenetic signal with categorical traits and tree uncertainty
title_fullStr Testing phylogenetic signal with categorical traits and tree uncertainty
title_full_unstemmed Testing phylogenetic signal with categorical traits and tree uncertainty
title_short Testing phylogenetic signal with categorical traits and tree uncertainty
title_sort testing phylogenetic signal with categorical traits and tree uncertainty
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374490/
https://www.ncbi.nlm.nih.gov/pubmed/37490431
http://dx.doi.org/10.1093/bioinformatics/btad433
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