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Bayesian inference of ancestral recombination graphs
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Mahmoudi, Ali, Koskela, Jere, Kelleher, Jerome, Chan, Yao-ban and Balding, David (2022) Bayesian inference of ancestral recombination graphs. PLoS Computational Biology, 18 (3). e1009960. doi:10.1371/journal.pcbi.1009960 ISSN 1553-7358.
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Official URL: https://doi.org/10.1371/journal.pcbi.1009960
Abstract
We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Journal or Publication Title: | PLoS Computational Biology | ||||||
Publisher: | Public Library of Science | ||||||
ISSN: | 1553-7358 | ||||||
Official Date: | 9 March 2022 | ||||||
Dates: |
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Volume: | 18 | ||||||
Number: | 3 | ||||||
Article Number: | e1009960 | ||||||
DOI: | 10.1371/journal.pcbi.1009960 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 25 February 2022 | ||||||
Date of first compliant Open Access: | 12 April 2022 | ||||||
RIOXX Funder/Project Grant: |
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