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Distance metrics for ranked evolutionary trees
Genealogical tree modeling is essential for estimating evolutionary parameters in population genetics and phylogenetics. Recent mathematical results concerning ranked genealogies without leaf labels unlock opportunities in the analysis of evolutionary trees. In particular, comparisons between ranked...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682335/ https://www.ncbi.nlm.nih.gov/pubmed/33139566 http://dx.doi.org/10.1073/pnas.1922851117 |
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author | Kim, Jaehee Rosenberg, Noah A. Palacios, Julia A. |
author_facet | Kim, Jaehee Rosenberg, Noah A. Palacios, Julia A. |
author_sort | Kim, Jaehee |
collection | PubMed |
description | Genealogical tree modeling is essential for estimating evolutionary parameters in population genetics and phylogenetics. Recent mathematical results concerning ranked genealogies without leaf labels unlock opportunities in the analysis of evolutionary trees. In particular, comparisons between ranked genealogies facilitate the study of evolutionary processes of different organisms sampled at multiple time periods. We propose metrics on ranked tree shapes and ranked genealogies for lineages isochronously and heterochronously sampled. Our proposed tree metrics make it possible to conduct statistical analyses of ranked tree shapes and timed ranked tree shapes or ranked genealogies. Such analyses allow us to assess differences in tree distributions, quantify estimation uncertainty, and summarize tree distributions. We show the utility of our metrics via simulations and an application in infectious diseases. |
format | Online Article Text |
id | pubmed-7682335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-76823352020-12-01 Distance metrics for ranked evolutionary trees Kim, Jaehee Rosenberg, Noah A. Palacios, Julia A. Proc Natl Acad Sci U S A Biological Sciences Genealogical tree modeling is essential for estimating evolutionary parameters in population genetics and phylogenetics. Recent mathematical results concerning ranked genealogies without leaf labels unlock opportunities in the analysis of evolutionary trees. In particular, comparisons between ranked genealogies facilitate the study of evolutionary processes of different organisms sampled at multiple time periods. We propose metrics on ranked tree shapes and ranked genealogies for lineages isochronously and heterochronously sampled. Our proposed tree metrics make it possible to conduct statistical analyses of ranked tree shapes and timed ranked tree shapes or ranked genealogies. Such analyses allow us to assess differences in tree distributions, quantify estimation uncertainty, and summarize tree distributions. We show the utility of our metrics via simulations and an application in infectious diseases. National Academy of Sciences 2020-11-17 2020-11-02 /pmc/articles/PMC7682335/ /pubmed/33139566 http://dx.doi.org/10.1073/pnas.1922851117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Kim, Jaehee Rosenberg, Noah A. Palacios, Julia A. Distance metrics for ranked evolutionary trees |
title | Distance metrics for ranked evolutionary trees |
title_full | Distance metrics for ranked evolutionary trees |
title_fullStr | Distance metrics for ranked evolutionary trees |
title_full_unstemmed | Distance metrics for ranked evolutionary trees |
title_short | Distance metrics for ranked evolutionary trees |
title_sort | distance metrics for ranked evolutionary trees |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682335/ https://www.ncbi.nlm.nih.gov/pubmed/33139566 http://dx.doi.org/10.1073/pnas.1922851117 |
work_keys_str_mv | AT kimjaehee distancemetricsforrankedevolutionarytrees AT rosenbergnoaha distancemetricsforrankedevolutionarytrees AT palaciosjuliaa distancemetricsforrankedevolutionarytrees |