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Estimating Bayesian Phylogenetic Information Content
Measuring the phylogenetic information content of data has a long history in systematics. Here we explore a Bayesian approach to information content estimation. The entropy of the posterior distribution compared with the entropy of the prior distribution provides a natural way to measure information...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066063/ https://www.ncbi.nlm.nih.gov/pubmed/27155008 http://dx.doi.org/10.1093/sysbio/syw042 |
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author | Lewis, Paul O. Chen, Ming-Hui Kuo, Lynn Lewis, Louise A. Fučíková, Karolina Neupane, Suman Wang, Yu-Bo Shi, Daoyuan |
author_facet | Lewis, Paul O. Chen, Ming-Hui Kuo, Lynn Lewis, Louise A. Fučíková, Karolina Neupane, Suman Wang, Yu-Bo Shi, Daoyuan |
author_sort | Lewis, Paul O. |
collection | PubMed |
description | Measuring the phylogenetic information content of data has a long history in systematics. Here we explore a Bayesian approach to information content estimation. The entropy of the posterior distribution compared with the entropy of the prior distribution provides a natural way to measure information content. If the data have no information relevant to ranking tree topologies beyond the information supplied by the prior, the posterior and prior will be identical. Information in data discourages consideration of some hypotheses allowed by the prior, resulting in a posterior distribution that is more concentrated (has lower entropy) than the prior. We focus on measuring information about tree topology using marginal posterior distributions of tree topologies. We show that both the accuracy and the computational efficiency of topological information content estimation improve with use of the conditional clade distribution, which also allows topological information content to be partitioned by clade. We explore two important applications of our method: providing a compelling definition of saturation and detecting conflict among data partitions that can negatively affect analyses of concatenated data. [Bayesian; concatenation; conditional clade distribution; entropy; information; phylogenetics; saturation.] |
format | Online Article Text |
id | pubmed-5066063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-50660632016-10-18 Estimating Bayesian Phylogenetic Information Content Lewis, Paul O. Chen, Ming-Hui Kuo, Lynn Lewis, Louise A. Fučíková, Karolina Neupane, Suman Wang, Yu-Bo Shi, Daoyuan Syst Biol Regular Articles Measuring the phylogenetic information content of data has a long history in systematics. Here we explore a Bayesian approach to information content estimation. The entropy of the posterior distribution compared with the entropy of the prior distribution provides a natural way to measure information content. If the data have no information relevant to ranking tree topologies beyond the information supplied by the prior, the posterior and prior will be identical. Information in data discourages consideration of some hypotheses allowed by the prior, resulting in a posterior distribution that is more concentrated (has lower entropy) than the prior. We focus on measuring information about tree topology using marginal posterior distributions of tree topologies. We show that both the accuracy and the computational efficiency of topological information content estimation improve with use of the conditional clade distribution, which also allows topological information content to be partitioned by clade. We explore two important applications of our method: providing a compelling definition of saturation and detecting conflict among data partitions that can negatively affect analyses of concatenated data. [Bayesian; concatenation; conditional clade distribution; entropy; information; phylogenetics; saturation.] Oxford University Press 2016-11 2016-05-06 /pmc/articles/PMC5066063/ /pubmed/27155008 http://dx.doi.org/10.1093/sysbio/syw042 Text en © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Regular Articles Lewis, Paul O. Chen, Ming-Hui Kuo, Lynn Lewis, Louise A. Fučíková, Karolina Neupane, Suman Wang, Yu-Bo Shi, Daoyuan Estimating Bayesian Phylogenetic Information Content |
title | Estimating Bayesian Phylogenetic Information Content |
title_full | Estimating Bayesian Phylogenetic Information Content |
title_fullStr | Estimating Bayesian Phylogenetic Information Content |
title_full_unstemmed | Estimating Bayesian Phylogenetic Information Content |
title_short | Estimating Bayesian Phylogenetic Information Content |
title_sort | estimating bayesian phylogenetic information content |
topic | Regular Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066063/ https://www.ncbi.nlm.nih.gov/pubmed/27155008 http://dx.doi.org/10.1093/sysbio/syw042 |
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