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Coalescent Simulation with msprime.

Jerome Kelleher1, Konrad Lohse2

  • 1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. jerome.kelleher@bdi.ox.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|January 25, 2020
PubMed
Summary
This summary is machine-generated.

The msprime library offers scalable coalescent simulations for population genetics research. It enables efficient processing of simulation results and flexible model construction, including approximate Bayesian computation.

Keywords:
Coalescent theoryPopulation geneticsPythonSimulation

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Area of Science:

  • Population genetics
  • Computational biology
  • Bioinformatics

Background:

  • Coalescent simulation is crucial for understanding population genetic processes.
  • Existing tools may lack scalability for complex demographic models.
  • Efficient analysis of simulation data is essential for modern research.

Purpose of the Study:

  • To introduce the msprime library for scalable coalescent simulations.
  • To demonstrate msprime's capabilities in constructing and processing population genetic models.
  • To showcase the library's flexibility for advanced analyses like approximate Bayesian computation.

Main Methods:

  • Utilizing the msprime Python API for defining coalescent models.
  • Implementing population structure and demographic scenarios.
  • Processing simulation outputs for statistical analysis.
  • Developing an approximate Bayesian computation method using msprime.

Main Results:

  • msprime provides significant scalability for coalescent simulations.
  • The library allows for efficient construction of complex demographic models.
  • Simulation results can be processed rapidly for downstream analyses.
  • A functional approximate Bayesian computation method was implemented concisely.

Conclusions:

  • msprime offers a powerful and efficient platform for population genetics research.
  • The library enhances the feasibility of large-scale simulations and complex analyses.
  • Its Python API facilitates accessible and flexible model implementation.