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PopPlanner: visually constructing demographic models for simulation.

Gregory B Ewing1, Pauline A Reiff1, Jeffrey D Jensen1

  • 1School of Life Sciences, École Polytechnique Fédérale de Lausanne Lausanne, Switzerland.

Frontiers in Genetics
|May 9, 2015
PubMed
Summary

PopPlanner simplifies complex population genetics by offering a graphical interface for building and inspecting demographic models. This tool reduces errors and enhances accessibility for coalescent simulations.

Keywords:
coalescentdemographicspopulation geneticssimulation

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Coalescent simulation programs are essential for modeling population genetics.
  • Current methods for defining complex demographic models using text files or command lines are error-prone and difficult to use.
  • A user-friendly interface is needed to simplify model construction and modification.

Purpose of the Study:

  • To introduce PopPlanner, a graphical user interface (GUI) based tool for constructing and inspecting demographic models.
  • To simplify the creation, modification, and error-checking of models for coalescent simulations.
  • To enhance accessibility for a wider user base in population genetics research.

Main Methods:

  • PopPlanner utilizes a GUI with intuitive mouse-based interactions for model design.
  • The software focuses on compatibility with popular command-line coalescent simulators like ms and msms.
  • It supports both the creation of new command lines and the identification of errors in existing ones.

Main Results:

  • PopPlanner enables visual construction and inspection of complex demographic models.
  • The tool aids in finding errors within existing simulation command lines.
  • Graphical output features facilitate editing and exporting publication-quality figures.

Conclusions:

  • PopPlanner significantly simplifies the process of defining and validating complex demographic models for coalescent simulations.
  • The GUI-based approach enhances usability and reduces errors, making advanced population genetics accessible to more researchers.
  • The software's features support efficient model building and high-quality data visualization for publication.