On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo
For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC). Phylogenetic networks are an extension of phylogenetic trees that can contain reticulate nodes, which allow to model complex biological events such as horizontal gene transfer, hybridization and introgression. We present a novel way to compute the likelihood of biallelic markers sampled along genomes whose evolution involved such events. This likelihood computation is at the heart of a Bayesian network inference method called SnappNet, as it extends the Snapp method inferring evolutionary trees under the multispecies coalescent model, to networks. SnappNet is available as a package of the well-known beast 2 software. Recently, the MCMC_BiMarkers method, implemented in PhyloNet, also extended Snapp to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SnappNet relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of SnappNet and MCMC_BiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SnappNet is more accurate than MCMC_BiMarkers on more complex network scenarios. Also, on complex networks, SnappNet is found to be extremely faster than MCMC_BiMarkers in terms of time required for the likelihood computation. We finally illustrate SnappNet performances on a rice data set. SnappNet infers a scenario that is consistent with previous results and provides additional understanding of rice evolution.
Main Authors: | , , , , , , , |
---|---|
Format: | article biblioteca |
Language: | eng |
Subjects: | modèle mathématique, modèle de simulation, génomique, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_92382, |
Online Access: | http://agritrop.cirad.fr/600263/ http://agritrop.cirad.fr/600263/1/600263.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-cirad-fr-600263 |
---|---|
record_format |
koha |
spelling |
dig-cirad-fr-6002632024-04-24T11:58:29Z http://agritrop.cirad.fr/600263/ http://agritrop.cirad.fr/600263/ On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo. Rabier Charles-Elie, Berry Vinvent, Stoltz Marnus, Santos João D., Wang Wensheng, Glaszmann Jean-Christophe, Pardi Fabio, Scornavacca Céline. 2021. PLoS Computational Biology, 17 (9):e1008380, 39 p.https://doi.org/10.1371/journal.pcbi.1008380 <https://doi.org/10.1371/journal.pcbi.1008380> On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo Rabier, Charles-Elie Berry, Vinvent Stoltz, Marnus Santos, João D. Wang, Wensheng Glaszmann, Jean-Christophe Pardi, Fabio Scornavacca, Céline eng 2021 PLoS Computational Biology modèle mathématique modèle de simulation génomique http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_92382 For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC). Phylogenetic networks are an extension of phylogenetic trees that can contain reticulate nodes, which allow to model complex biological events such as horizontal gene transfer, hybridization and introgression. We present a novel way to compute the likelihood of biallelic markers sampled along genomes whose evolution involved such events. This likelihood computation is at the heart of a Bayesian network inference method called SnappNet, as it extends the Snapp method inferring evolutionary trees under the multispecies coalescent model, to networks. SnappNet is available as a package of the well-known beast 2 software. Recently, the MCMC_BiMarkers method, implemented in PhyloNet, also extended Snapp to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SnappNet relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of SnappNet and MCMC_BiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SnappNet is more accurate than MCMC_BiMarkers on more complex network scenarios. Also, on complex networks, SnappNet is found to be extremely faster than MCMC_BiMarkers in terms of time required for the likelihood computation. We finally illustrate SnappNet performances on a rice data set. SnappNet infers a scenario that is consistent with previous results and provides additional understanding of rice evolution. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/600263/1/600263.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1371/journal.pcbi.1008380 10.1371/journal.pcbi.1008380 info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pcbi.1008380 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1371/journal.pcbi.1008380 info:eu-repo/semantics/reference/purl/https://github.com/rabier/MySnappNet info:eu-repo/semantics/dataset/purl/https://figshare.com/articles/journal_contribution/Supplementary_material_for_the_manuscript_/16568780 |
institution |
CIRAD FR |
collection |
DSpace |
country |
Francia |
countrycode |
FR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cirad-fr |
tag |
biblioteca |
region |
Europa del Oeste |
libraryname |
Biblioteca del CIRAD Francia |
language |
eng |
topic |
modèle mathématique modèle de simulation génomique http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_92382 modèle mathématique modèle de simulation génomique http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_92382 |
spellingShingle |
modèle mathématique modèle de simulation génomique http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_92382 modèle mathématique modèle de simulation génomique http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_92382 Rabier, Charles-Elie Berry, Vinvent Stoltz, Marnus Santos, João D. Wang, Wensheng Glaszmann, Jean-Christophe Pardi, Fabio Scornavacca, Céline On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo |
description |
For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC). Phylogenetic networks are an extension of phylogenetic trees that can contain reticulate nodes, which allow to model complex biological events such as horizontal gene transfer, hybridization and introgression. We present a novel way to compute the likelihood of biallelic markers sampled along genomes whose evolution involved such events. This likelihood computation is at the heart of a Bayesian network inference method called SnappNet, as it extends the Snapp method inferring evolutionary trees under the multispecies coalescent model, to networks. SnappNet is available as a package of the well-known beast 2 software. Recently, the MCMC_BiMarkers method, implemented in PhyloNet, also extended Snapp to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SnappNet relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of SnappNet and MCMC_BiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SnappNet is more accurate than MCMC_BiMarkers on more complex network scenarios. Also, on complex networks, SnappNet is found to be extremely faster than MCMC_BiMarkers in terms of time required for the likelihood computation. We finally illustrate SnappNet performances on a rice data set. SnappNet infers a scenario that is consistent with previous results and provides additional understanding of rice evolution. |
format |
article |
topic_facet |
modèle mathématique modèle de simulation génomique http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_92382 |
author |
Rabier, Charles-Elie Berry, Vinvent Stoltz, Marnus Santos, João D. Wang, Wensheng Glaszmann, Jean-Christophe Pardi, Fabio Scornavacca, Céline |
author_facet |
Rabier, Charles-Elie Berry, Vinvent Stoltz, Marnus Santos, João D. Wang, Wensheng Glaszmann, Jean-Christophe Pardi, Fabio Scornavacca, Céline |
author_sort |
Rabier, Charles-Elie |
title |
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo |
title_short |
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo |
title_full |
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo |
title_fullStr |
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo |
title_full_unstemmed |
On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo |
title_sort |
on the inference of complex phylogenetic networks by markov chain monte-carlo |
url |
http://agritrop.cirad.fr/600263/ http://agritrop.cirad.fr/600263/1/600263.pdf |
work_keys_str_mv |
AT rabiercharleselie ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo AT berryvinvent ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo AT stoltzmarnus ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo AT santosjoaod ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo AT wangwensheng ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo AT glaszmannjeanchristophe ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo AT pardifabio ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo AT scornavaccaceline ontheinferenceofcomplexphylogeneticnetworksbymarkovchainmontecarlo |
_version_ |
1798165074201804800 |