arzwa / Whale.jl

Bayesian gene tree reconciliation and WGD inference using amalgamated likelihood estimation

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Whale: Bayesian gene tree reconciliation and whole-genome duplication inference by amalgamated likelihood estimation

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Whale.jl is a julia library for joint inference of gene tree topologies and their reconciliations to a species tree. Whale uses the amalgamation method of Szollosi et al. (2014) to efficiently compute the marginal likelihood of the gene family under a duplication-loss model of gene family evolution over a distribution of tree topologies. Whale also implements a duplication-loss and whole-genome duplication (DLWGD) model (Rabier et al. 2014, Zwaenepoel et al. 2019). The latter can be used for the inference of ancient whole-genome duplications (WGDs) from gene trees while taking into account gene tree and reconciliation uncertainty.

More specifically, this library can be used to

  • Statistically assess hypothetical whole-genome duplication (WGD) events in a species phylogeny using maximum likelihood estimation (MLE) or Bayesian inference.
  • Infer lineage-specific gene duplication and loss rates for a species phylogeny.
  • Infer high-quality (reconciled) gene trees given a known species tree using Bayesian gene tree reconciliation.
  • Conduct Bayesian orthology inference.
  • All of the above at once.

For more information and guidance, please consult the docs. If you are not a julia coder, you might want to get some minimal familiarity with the Julia REPL and its package manager when using Whale, see the julia docs.

Note that the scripts in the scripts directory might be helpful to prepare data for Whale analyses.

Citation

If you use Whale, please cite:

Zwaenepoel, A. and Van de Peer, Y., 2019. Inference of Ancient Whole-Genome Duplications and the Evolution of Gene Duplication and Loss Rates. Molecular biology and evolution, 36(7), pp.1384-1404.


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Bayesian gene tree reconciliation and WGD inference using amalgamated likelihood estimation

License:GNU Lesser General Public License v2.1


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