Journal article
Bayesian inference of phylogenetic distances: revisiting the eigenvalue approach
- Abstract:
- Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more complex biological phenomena, such as variations in lineage-specific evolutionary rates over time (heterotachy), other approaches such as the GTR with rate variation (GTR +Γ ) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for Bayesian analysis. In this paper, we derive a hybrid approach between these two methods, incorporating Γ(α,α) -distributed rate variation and heterotachy into a hierarchical Bayesian GTR-style framework. Our approach is differentiable and amenable to both stochastic gradient descent for optimisation and Hamiltonian Markov chain Monte Carlo for Bayesian inference. We show the utility of our approach by studying hypotheses regarding the origins of the eukaryotic cell within the context of a universal tree of life and find evidence for a two-domain theory.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 1.5MB, Terms of use)
-
- Publisher copy:
- 10.1007/s11538-024-01403-z
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- DTP Studentship (University of Oxford)
- Publisher:
- Springer Nature
- Journal:
- Bulletin of Mathematical Biology More from this journal
- Volume:
- 87
- Issue:
- 2
- Article number:
- 32
- Place of publication:
- United States
- Publication date:
- 2025-01-23
- Acceptance date:
- 2024-12-13
- DOI:
- EISSN:
-
1522-9602
- ISSN:
-
0092-8240
- Pmid:
-
39847307
- Language:
-
English
- Keywords:
- Pubs id:
-
2080309
- Local pid:
-
pubs:2080309
- Deposit date:
-
2025-02-01
- ARK identifier:
Terms of use
- Copyright holder:
- Penn et al
- Copyright date:
- 2025
- Rights statement:
- © 2025 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record