nnTreeVB: a Neural Network-based Variational Bayesian Framework for Phylogenetic Parameter Estimation
The configuration files of the experiments performed in the paper Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference can be found in the repository nnTreeVB_Exp.
nntreevb_learn_prior.ipynb is an example of using nnTreeVB to learn prior densities of branch lengths with a JC69 substitution model.
nnTreeVB is actively in the development phase. The names of the different package entities and the default values are subject to change. Please, feel free to contact me if you want to refactor, add, or discuss a feature.
nnTreeVB is a deep variationl model that simultaneously estimates evolutionary parameters using a phylogenetic tree and multiple sequence alignment
The nnTreeVB package depends on Python packages that could be installed with pip and conda.
The main packages that nnTreeVB uses are pytorch, numpy and biopython and ete3.
You can find the complete list of dependencies in the requirements.txt file.
This file is used automatically by setup.py to install nnTreeVB using pip or conda.
nnTreeVB is developed in Python3 and can be easily installed using pip. I recommend installing the package in a separate virtual environment (virtualenv, venv, conda env, ect.).
I haven't tested the installation yet using conda.
Once the virtual environment is created, nnTreeVB can be installed from the git repository directly through pip:
python -m pip install git+https://github.com/maremita/nnTreeVB.git
or by cloning the repository and installing with pip:
git clone https://github.com/maremita/nnTreeVB.git
cd nnTreeVB
pip install .
Classes and functions implemented in the nnTreeVB package can be called and used in Python scripts and notebooks.
This is an example of how to use nnTreeVB to fit an nnTreeVB_GTR to estimate branch lengths, substitution rates and relative frequencies.
Experiments for the assessment of nnTreeVB can be found in the project nnTreeVB_Exp.
I am preparing the main nnTreeVB manuscript. In the meantime, if you want to refer to the framework, you can cite this preprint:
@InProceedings{remita2023learn_vbprior,
author={Remita, Amine M. and Vitae, Golrokh and Diallo, Abdoulaye Banir{\'e}},
editor={Jahn, Katharina and Vina{\v{r}}, Tom{\'a}{\v{s}}},
title={Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference},
booktitle={Comparative Genomics},
year={2023},
publisher={Springer Nature Switzerland},
address={Cham},
pages={112--130},
isbn={978-3-031-36911-7},
doi={10.1007/978-3-031-36911-7_8}
}
The nnTreeVB package including the modules and the scripts is distributed under the MIT License.
If you have any questions, please do not hesitate to contact:
- Amine Remita remita.amine@courrier.uqam.ca