Open AccessOn the inference of complex phylogenetic networks by Markov Chain Monte-Carlo
Author(s) -
Charles-Elie Rabier,
Vincent Berry,
Marnus Stoltz,
João dos Santos,
Wensheng Wang,
Jean-Christophe Glaszmann,
Fabio Pardi,
Céline Scornavacca
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1008380
Subject(s) - coalescent theory , markov chain monte carlo , inference , computer science , phylogenetic tree , phylogenetic network , bayesian probability , bayesian inference , prior probability , artificial intelligence , theoretical computer science , biology , machine learning , genetics , gene
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 S napp N et , as it extends the S napp method inferring evolutionary trees under the multispecies coalescent model, to networks. S napp N et is available as a package of the well-known beast 2 software. Recently, the MCMC_BiMarkers method, implemented in PhyloNet, also extended S napp 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, S napp N et relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of S napp N et and MCMC_BiMarkers . We show that both methods enjoy similar abilities to recover simple networks, but S napp N et is more accurate than MCMC_BiMarkers on more complex network scenarios. Also, on complex networks, S napp N et is found to be extremely faster than MCMC_BiMarkers in terms of time required for the likelihood computation. We finally illustrate S napp N et performances on a rice data set. S napp N et infers a scenario that is consistent with previous results and provides additional understanding of rice evolution.