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adaPop: bayesian inference of dependent population dynamics in coalescent models

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dc.contributor.author Cappello, Lorenzo
dc.contributor.author Kim, Jaehee
dc.contributor.author Palacios, Julia A.
dc.date.accessioned 2023-06-20T06:14:18Z
dc.date.available 2023-06-20T06:14:18Z
dc.date.issued 2023
dc.identifier.citation Cappello L, Kim J, Palacios JA. adaPop: bayesian inference of dependent population dynamics in coalescent models. PLoS Comput Biol. 2023 Mar 20;19(3):e1010897. DOI: 10.1371/journal.pcbi.1010897
dc.identifier.issn 1553-734X
dc.identifier.uri http://hdl.handle.net/10230/57237
dc.description Includes supplementary materials for the online appendix.
dc.description.abstract The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLOS Computational Biology. 2023 Mar 20;19(3):e1010897
dc.relation.isreferencedby https://doi.org/10.1371/journal.pcbi.1010897.s001
dc.rights © 2023 Cappello et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Pandèmia de COVID-19, 2020-
dc.subject.other Càncer
dc.subject.other Tumors
dc.subject.other Genètica -- Aspectes socials
dc.title adaPop: bayesian inference of dependent population dynamics in coalescent models
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1010897
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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