SimRA

SimRA simulates complex evolutionary scenarios involving multiple populations with subdivision and admixture by using random graph algorithms to model ancestral recombination graphs (ARGs) for population-genomic analyses.

Key Features:

• Ancestral Recombination Graph (ARG): SimRA leverages ARGs to represent the genealogical history of sampled genomes, explicitly capturing recombination events across generations.
• Random Graph Model: SimRA employs random graph algorithms to simulate population evolution, improving time and space efficiency relative to traditional single-population algorithms.
• Parameter Estimation: SimRA derives closed-form expressions for expected ARG characteristics—including graph height, number of recombinations, mutations, and population diversity—expressed in terms of model parameters.
• Accuracy and Compactness: SimRA produces compact representations of ARGs while maintaining accuracy, a balance supported by experimental evaluations.

Scientific Applications:

• Hypothesis testing and ARG reconstruction: SimRA provides simulated ARGs for testing evolutionary hypotheses and reconstructing genealogical histories.
• Modeling subdivision and admixture: SimRA models subdivision, admixture, and multi-population dynamics to study genetic structure and population history.
• Recombination and mutation analysis: SimRA enables investigation of recombination dynamics and mutation patterns across simulated genomes.
• Parameter exploration and study design: SimRA supports parameter specification and exploration using closed-form expectations to inform experimental design and inference.

Methodology:

SimRA implements a novel algorithmic approach based on random graphs to construct Ancestral Recombination Graphs for multiple-population evolution models and derives closed-form expressions for expected ARG characteristics (graph height, number of recombinations, mutations, population diversity), improving computational time and space efficiency.