DIYABC-RF
DIYABC-RF [1] is an inference software 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.
If you are looking for the previous DIYABC V2.1: please check here, however we are not maintaining this version anymore.
Documentation
The complete software documentation for the graphical user interface (GUI) and the command line interface (CLI) is available on the dedicated page.
Graphical User Interface
DIYABC-RF can be used through a graphical user interface (GUI) called DIYABC-RF GUI, please visit the dedicated page for more
information (and especially installation instructions).
DIYABC-RF GUI is a simple and user friendly interface for the DIYABC-RF
framework.
⚠️ Due to work overload, the development, support and maintenance for the project
DIYABC-RF GUIis currently paused. Feel free to contact us (by submitting an issue) if you want to take over the development for this project. Pull request are also welcome to fix bug or implement missing functionalities. You can still use the command line version of the software which is still maintained/supported. Check the documentation page here. Thanks ⚠️
ℹ️ If you encounter any issue, please visit and report bug at DIYABC-RF GUI issue tracker.
Command line interface
For advanced users, it is possible to use DIYABC-RF as a command-line
pipeline based on the command-line software diyabc
and abcranger.
Please visit the dedicated page for more details.
DIYABC-RF project examples
Please visit the dedicated page to find examples of DIYABC-RF data analysis projects, including data files and related DIYABC-RF configuration files.
Reference
[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>
diyabc github project: https://github.com/diyabc