CoalMiner: a coalescent model generator for fastsimcoal2.

Area of Science:

• Population Genetics
• Evolutionary Biology
• Computational Biology

Background:

• Demographic inference using the Site Frequency Spectrum (SFS) is limited by the number and complexity of models.
• Exploring diverse demographic histories is crucial for understanding evolutionary processes.

Purpose of the Study:

• To introduce CoalMiner, a novel coalescent model generator for fastsimcoal2.
• To facilitate the generation of biologically plausible demographic models for population genetic analyses.

Main Methods:

• CoalMiner employs a decision tree framework to generate demographic models.
• User input defines demographic parameters and histories for model generation.
• Generated models are compatible with the fastsimcoal2 pipeline.

Main Results:

• CoalMiner effectively expands the exploration of demographic model space.
• Simulations and empirical data demonstrate CoalMiner's utility as a helper tool.
• The tool enhances the process of demographic inference.

Conclusions:

• CoalMiner is an effective tool for generating and exploring demographic models in population genetics.
• The Python-based generator is freely available with tutorials for ease of use.