EBG: Educated Bootstrap Guesser

Documentation: https://github.com/wiegertj/EBG/wiki

Description

EBG is a Python tool for predicting the Felsenstein Bootstrap Support of phylogenies inferred by RAxML-NG. It was trained on empirical datasets from TreeBASE and can use both AA and DNA data.

Installation

Using conda

The latest version of EBG can easily be installed via conda:

conda install ebg -c conda-forge

Using pip

pip install ebg

Usage Example

A simple command line call of EBG looks like this:

ebg -msa /test/example.fasta -tree /test/example.bestTree -model /test/example.bestModel -t b -o test 

This command will use the MSA in fasta format, and the best tree inferred with RAxML-NG and the model. By selecting -t b(oth) EBG will output the bootstrap predictions as well as the probabilities for exceeding different bootstrap thresholds (70, 75, 80, 85). The results will be stored in a folder called test.

Please keep in mind that EBG requires an installation of RAxML-NG. By default, it uses the command raxml-ng. If your RAxML-NG installation is not part of the PATH variable, you can specify the path to the RAxML-NG binary file with the parameter -raxmlng PATH_TO_RAXMLNG.

Citation

If you are using EBG for your publication, please cite our published paper in Molecular Biology and Evolution: EBG Paper

References

• A. M. Kozlov, D. Darriba, T. Flouri, B. Morel, and A. Stamatakis (2019) RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference Bioinformatics, 35(21): 4453–4455. https://doi.org/10.1093/bioinformatics/btz305