DIYABC-RF GUI

Machine learning
Genetics
GUI
Graphical User Interface for DYIABC-RF software
Author

Ghislain Durif

Published

July 10, 2020

Doc Publication Code

(full development and maintenance of diyabcGUI R package)

Collaboration with:

  • François-David Collin (IMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, France)
  • Louis Raynal (IMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, France)
  • Eric Lombaert (ISA, INRAE, CNRS, Univ Côte d’Azur, Sophia Antipolis, France)
  • Mathieu Gautier (CBGP, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France)
  • Renaud Vitalis (CBGP, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France)
  • Jean-Michel Marin (IMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, France)
  • Arnaud Estoup (CBGP, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France)

Graphical user interface (GUI) for the DIYABC-RF software [1], called DIYABC-RF GUI.

Please check the project website (https://diyabc.github.io/) for additional information and detailed documentation. See https://github.com/diyabc/diyabcGUI for specific questions/issues related to the GUI.

DIYABC-RF GUI is available as a standalone application, or in a R package called diyabcGUI as a shiny web app. You can either install the standalone app, or the diyabcGUI package and run the DIYABC-RF GUI as a standard shiny app.

DIYABC-RF GUI is a set of tools implementing Approximate Bayesian Computation (ABC) combined with supervised machine learning based on Random Forests (RF), for model choice and parameter inference in the context of population genetics analysis. diyabcGUI provides a user-friendly interface for command-line softwares diyabc (https://github.com/diyabc/diyabc) and abcranger (https://github.com/diyabc/abcranger).

[1] Collin F.-D., Durif G., Raynal L., Gautier M., Vitalis R., Lombaert E., Marin J.-M., Estoup A., 2021, Extending Approximate Bayesian Computation with Supervised Machine Learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest. Molecular Ecology Resources, Wiley/Blackwell, 21(8), pp. 2598–2613. <doi/10.1111/1755-0998.13413> <hal-03229207>

Programming:

  • R Shiny

Keywords:

  • Approximate Bayesian Computation
  • Model or scenario selection
  • Parameter estimation
  • Population genetics
  • Random Forest
  • Supervised Machine Learning