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Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure
Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can ske...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061232/ https://www.ncbi.nlm.nih.gov/pubmed/32022857 http://dx.doi.org/10.1093/gbe/evaa022 |
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author | Koch, Hillary DeGiorgio, Michael |
author_facet | Koch, Hillary DeGiorgio, Michael |
author_sort | Koch, Hillary |
collection | PubMed |
description | Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI’s performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community. |
format | Online Article Text |
id | pubmed-7061232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-70612322020-03-12 Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure Koch, Hillary DeGiorgio, Michael Genome Biol Evol Research Article Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI’s performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community. Oxford University Press 2020-02-05 /pmc/articles/PMC7061232/ /pubmed/32022857 http://dx.doi.org/10.1093/gbe/evaa022 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Koch, Hillary DeGiorgio, Michael Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure |
title | Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure |
title_full | Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure |
title_fullStr | Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure |
title_full_unstemmed | Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure |
title_short | Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure |
title_sort | maximum likelihood estimation of species trees from gene trees in the presence of ancestral population structure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061232/ https://www.ncbi.nlm.nih.gov/pubmed/32022857 http://dx.doi.org/10.1093/gbe/evaa022 |
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