On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo

PLoS Comput Biol. 2021 Sep 3;17(9):e1008380. doi: 10.1371/journal.pcbi.1008380. eCollection 2021 Sep.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bayes Theorem
  • Computational Biology / methods
  • Evolution, Molecular
  • Genes, Plant
  • Likelihood Functions
  • Markov Chains*
  • Monte Carlo Method*
  • Oryza / classification
  • Oryza / genetics
  • Phylogeny*

Grants and funding

C.E.R., C.S., J.G., J.S., V.B. were partially funded by the Genome Harvest project (reference ID 1504-006, Labex Agro: ANR-10-LABX-0001-01) for study design, data collection, data analysis. C.E.R., C.S. and V.B. were also partially funded by a KIM Data & Life Sciences project (I-SITE MUSE: ANR-16-IDEX-0006) for data collection, data analysis. C.S. and M.S. were funded by the French Agence Nationale de la Recherche, through the CoCoAlSeq project (ANR-19-CE45-0012) for study design, data collection, data analysis. C.E.R., F.P. and V.B. were funded by the ATGC bioinformatic platform, a member of both the “France Génomique”network (ANR-10-INBS-0009) and the Institut Français de Bioinformatique (ANR-11-INBS-0013) for data collection and data analysis. C.E.R., C.S., M.S. and V.B. were funded by the French Agence Nationale de la Recherche, “Investissements d’Avenir” program, through the Montpellier Bioinformatics Biodiversity platform supported by the LabEx CeMEB (ANR-10-LABX-04-01) for data collection and data analysis. C.E.R. was also funded by the High Performance Computing Platform MESO@LR, financed by the Occitanie /Pyrénées-Méditerranée Region, Montpellier Mediterranean Metropole and the University of Montpellier for data collection and data analysis. C.E.R., J.G. and V.B. were also funded by the CIRAD - UMR AGAP HPC Data Center of the South Green Bioinformatics platform (http://www.south-green.fr/). for data collection and data analysis. J.G. was also funded by the French Agence Nationale de la Recherche, “Investissements d’Avenir” program, through the AdaptGrass project (reference ID170544IA, I-SITE MUSE: ANR-16-IDEX-0006) for study design, data collection and data analysis. W.W. was funded by the CGIAR Research Program on Rice Agrifood Systems (RICE) for study design and data collection.