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 effici...

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Autores principales:	Charles-Elie Rabier, Vincent Berry, Marnus Stoltz, João D Santos, Wensheng Wang, Jean-Christophe Glaszmann, Fabio Pardi, Celine Scornavacca
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/98f4faaef9124aaabff69bd651787efe
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spelling	oai:doaj.org-article:98f4faaef9124aaabff69bd651787efe2021-12-02T19:57:51ZOn the inference of complex phylogenetic networks by Markov Chain Monte-Carlo.1553-734X1553-735810.1371/journal.pcbi.1008380https://doaj.org/article/98f4faaef9124aaabff69bd651787efe2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008380https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358For 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.Charles-Elie RabierVincent BerryMarnus StoltzJoão D SantosWensheng WangJean-Christophe GlaszmannFabio PardiCeline ScornavaccaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1008380 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Charles-Elie Rabier Vincent Berry Marnus Stoltz João D Santos Wensheng Wang Jean-Christophe Glaszmann Fabio Pardi Celine Scornavacca 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
author	Charles-Elie Rabier Vincent Berry Marnus Stoltz João D Santos Wensheng Wang Jean-Christophe Glaszmann Fabio Pardi Celine Scornavacca
author_facet	Charles-Elie Rabier Vincent Berry Marnus Stoltz João D Santos Wensheng Wang Jean-Christophe Glaszmann Fabio Pardi Celine Scornavacca
author_sort	Charles-Elie Rabier
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.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/98f4faaef9124aaabff69bd651787efe
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On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo.

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