Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations]
Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Modern statistical methods involve n...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2023-11-01
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| Series: | Open Research Europe |
| Subjects: | |
| Online Access: | https://open-research-europe.ec.europa.eu/articles/3-204/v1 |
| _version_ | 1797263726927151104 |
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| author | Orlando Schwery Joëlle Barido-Sottani Chi Zhang Rachel C. M. Warnock April Marie Wright |
| author_facet | Orlando Schwery Joëlle Barido-Sottani Chi Zhang Rachel C. M. Warnock April Marie Wright |
| author_sort | Orlando Schwery |
| collection | DOAJ |
| description | Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov Chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them. |
| first_indexed | 2024-04-25T00:17:36Z |
| format | Article |
| id | doaj.art-4f50f579f47547be9697d08ca2ce3ef4 |
| institution | Directory Open Access Journal |
| issn | 2732-5121 |
| language | English |
| last_indexed | 2024-04-25T00:17:36Z |
| publishDate | 2023-11-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | Open Research Europe |
| spelling | doaj.art-4f50f579f47547be9697d08ca2ce3ef42024-03-13T01:00:00ZengF1000 Research LtdOpen Research Europe2732-51212023-11-01318012Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations]Orlando Schwery0Joëlle Barido-Sottani1https://orcid.org/0000-0002-5220-5468Chi Zhang2Rachel C. M. Warnock3April Marie Wright4Department of Biological Sciences, Southeastern Louisiana University, Hammond, Louisiana, 70402, USAInstitut de Biologie de l’ENS (IBENS), École normale supérieure, CNRS, INSERM, Université PSL, Paris, Île-de-France, 75005, FranceKey Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, 100044, ChinaGeoZentrum Nordbayern, Department of Geography and Geosciences, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Bavaria, 91054, GermanyDepartment of Biological Sciences, Southeastern Louisiana University, Hammond, Louisiana, 70402, USAPhylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov Chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them.https://open-research-europe.ec.europa.eu/articles/3-204/v1Bayesian phylogenetic inference MCMC troubleshooting phylogenetic inference software fossilized birth-death total-evidenceeng |
| spellingShingle | Orlando Schwery Joëlle Barido-Sottani Chi Zhang Rachel C. M. Warnock April Marie Wright Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations] Open Research Europe Bayesian phylogenetic inference MCMC troubleshooting phylogenetic inference software fossilized birth-death total-evidence eng |
| title | Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_full | Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_fullStr | Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_full_unstemmed | Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_short | Practical guidelines for Bayesian phylogenetic inference using Markov Chain Monte Carlo (MCMC) [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_sort | practical guidelines for bayesian phylogenetic inference using markov chain monte carlo mcmc version 1 peer review 2 approved 1 approved with reservations |
| topic | Bayesian phylogenetic inference MCMC troubleshooting phylogenetic inference software fossilized birth-death total-evidence eng |
| url | https://open-research-europe.ec.europa.eu/articles/3-204/v1 |
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