Allopolyploid Species Network Inference Models and Simulations

Graham Jones provides data and models for inferring evolutionary histories of allopolyploid species, which arise from hybridization. The work evaluates two new Bayesian models, AlloppMUL and AlloppNET, implemented in the BEAST framework. It includes simulation results and demonstrations on empirical data from the plant genera Pachycladon and Silene.

Use Cases

• Compare the accuracy of the AlloppMUL and AlloppNET models using simulation results to assess assumptions about hybridization events.
• Apply the implemented BEAST framework models to infer multi-labeled species trees from genomic data in genera like Pachycladon (Brassicaceae).
• Analyze evolutionary histories in allotetraploid species, such as those in the genus Silene (Caryophyllaceae), using the provided statistical inference models.

Strengths

• Includes empirical data from two distinct plant genera, Pachycladon and Silene, providing real-world test cases.
• Models are implemented within the established BEAST framework for Bayesian evolutionary analysis.
• The work was published in 2020, representing contemporary methodological research in phylogenetics.

Limitations

• The dataset scope is specific to a single hybridization event forming an allotetraploid, limiting applicability to other polyploid scenarios.
• Input details like row counts, column features, and file formats are unspecified, hindering assessment of data scale and structure.
• Relies on assumptions for the AlloppNET model, which may not hold for all empirical allopolyploid systems.

Provenance

Source

Graham Jones, via the Dryad repository.

Collection Method

Data generated from simulations and analysis of previously studied empirical data sets.

Freshness

Last updated on 2020-06-24.