Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics
(2023) In Systematic Biology 72(5). p.1199-1206- Abstract
Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncer‑ tainty of inferences and the possibility of accommodating multiple aspects of evolutionary processes in the models that are used. Unfortunately, Bayesian methods are computationally expensive, and typical applications involve at most a few hundred sequences. This is problematic in the age of rapidly expanding genomic data and increasing scope of evolutionary analyses, forcing researchers to resort to less accurate but faster methods, such as maximum parsimony and maximum like‑ lihood. Does this spell doom for... (More)
Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncer‑ tainty of inferences and the possibility of accommodating multiple aspects of evolutionary processes in the models that are used. Unfortunately, Bayesian methods are computationally expensive, and typical applications involve at most a few hundred sequences. This is problematic in the age of rapidly expanding genomic data and increasing scope of evolutionary analyses, forcing researchers to resort to less accurate but faster methods, such as maximum parsimony and maximum like‑ lihood. Does this spell doom for Bayesian methods? Not necessarily. Here, we discuss some recently proposed approaches that could help scale up Bayesian analyses of evolutionary problems considerably. We focus on two particular aspects: online phylogenetics, where new data sequences are added to existing analyses, and alternatives to Markov chain Monte Carlo (MCMC) for scalable Bayesian inference. We identify 5 specific challenges and discuss how they might be overcome. We believe that online phylogenetic approaches and Sequential Monte Carlo hold great promise and could potentially speed up tree inference by orders of magnitude. We call for collaborative efforts to speed up the development of methods for real‑time tree expansion through online phylogenetics. [Bayesian inference; MCMC; phylogeny; sequential Monte Carlo.]
(Less)
- author
- Truszkowski, Jakub ; Perrigo, Allison LU ; Broman, David ; Ronquist, Fredrik and Antonelli, Alexandre
- publishing date
- 2023-09-01
- type
- Contribution to journal
- publication status
- published
- in
- Systematic Biology
- volume
- 72
- issue
- 5
- pages
- 8 pages
- publisher
- Oxford University Press
- external identifiers
-
- pmid:37498209
- scopus:85177488121
- ISSN
- 1063-5157
- DOI
- 10.1093/sysbio/syad045
- language
- English
- LU publication?
- no
- additional info
- Funding Information: This work was supported by the Swedish Research Council (grants 2019-05191 to AA, 2018-04329 to DB, and 2021-04830 to FR) and by the Swedish Foundation for Strategic Research (grant number FFL15-0032 to DB). Publisher Copyright: © The Author(s) 2023.
- id
- 16dfc44e-49a6-4ba5-85ce-9d538888a028
- date added to LUP
- 2023-12-04 08:15:34
- date last changed
- 2024-06-26 11:10:50
@article{16dfc44e-49a6-4ba5-85ce-9d538888a028,
abstract = {{<p>Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncer‑ tainty of inferences and the possibility of accommodating multiple aspects of evolutionary processes in the models that are used. Unfortunately, Bayesian methods are computationally expensive, and typical applications involve at most a few hundred sequences. This is problematic in the age of rapidly expanding genomic data and increasing scope of evolutionary analyses, forcing researchers to resort to less accurate but faster methods, such as maximum parsimony and maximum like‑ lihood. Does this spell doom for Bayesian methods? Not necessarily. Here, we discuss some recently proposed approaches that could help scale up Bayesian analyses of evolutionary problems considerably. We focus on two particular aspects: online phylogenetics, where new data sequences are added to existing analyses, and alternatives to Markov chain Monte Carlo (MCMC) for scalable Bayesian inference. We identify 5 specific challenges and discuss how they might be overcome. We believe that online phylogenetic approaches and Sequential Monte Carlo hold great promise and could potentially speed up tree inference by orders of magnitude. We call for collaborative efforts to speed up the development of methods for real‑time tree expansion through online phylogenetics. [Bayesian inference; MCMC; phylogeny; sequential Monte Carlo.]</p>}},
author = {{Truszkowski, Jakub and Perrigo, Allison and Broman, David and Ronquist, Fredrik and Antonelli, Alexandre}},
issn = {{1063-5157}},
language = {{eng}},
month = {{09}},
number = {{5}},
pages = {{1199--1206}},
publisher = {{Oxford University Press}},
series = {{Systematic Biology}},
title = {{Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics}},
url = {{http://dx.doi.org/10.1093/sysbio/syad045}},
doi = {{10.1093/sysbio/syad045}},
volume = {{72}},
year = {{2023}},
}