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Speeding up inference of homologous recombination in bacteria
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Medina-Aguayo, F. J., Didelot, Xavier and Everitt, Richard G. (2024) Speeding up inference of homologous recombination in bacteria. Bayesian Analysis . doi:10.1214/23-BA1388 ISSN 1931-6690. (In Press)
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Official URL: https://doi.org/10.1214/23-BA1388
Abstract
Bacteria reproduce clonally but most species recombine frequently, so that the ancestral process is best captured using an ancestral recombination graph. This graph model is often too complex to be used in an inferential setup, but it can be approximated for example by the ClonalOrigin model. Inference in the ClonalOrigin model is performed via a Reversible-Jump Markov Chain Monte Carlo algorithm, which attempts to jointly explore: the recombination rate, the number of recombination events, the departure and arrival points on the clonal genealogy for each recombination event, and the range of genomic sites affected by each recombination event. However, the Reversible-Jump algorithm often performs poorly due to the complexity of the target distribution since it needs to explore spaces of different dimensions. Recent developments in Bayesian computation methodology have provided ways to improve existing methods and code, but are not well-known outside the statistics community. We show how exploiting one of these new computational methods can lead to faster inference under the ClonalOrigin model.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QH Natural history |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Statistics |
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Library of Congress Subject Headings (LCSH): | Genetics, Genetics -- Mathematical models, Genetic recombination , Markov processes, Monte Carlo method, Computational biology , Bacteria -- Evolution | |||||||||
Journal or Publication Title: | Bayesian Analysis | |||||||||
Publisher: | International Society for Bayesian Analysis | |||||||||
ISSN: | 1931-6690 | |||||||||
Official Date: | 2024 | |||||||||
Dates: |
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DOI: | 10.1214/23-BA1388 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | In Press | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Copyright Holders: | c 2023 International Society for Bayesian Analysis | |||||||||
Date of first compliant deposit: | 10 May 2023 | |||||||||
Date of first compliant Open Access: | 7 August 2023 | |||||||||
RIOXX Funder/Project Grant: |
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