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  • 标题:Distance metrics for ranked evolutionary trees
  • 本地全文:下载
  • 作者:Jaehee Kim ; Noah A. Rosenberg ; Julia A. Palacios
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2020
  • 卷号:117
  • 期号:46
  • 页码:28876-28886
  • DOI:10.1073/pnas.1922851117
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要: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.
  • 关键词:coalescent ; distance metric ; phylogenetics ; ranked genealogy ; ranked tree shape
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