Rapid discovery of novel prophages using biological feature engineering and machine learning
TBA
Prophage predictor based on gene features against a background.
Prophages are phages integrated into prokaryotic genomes that drive many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity. We present a novel fast and generalizing machine learning method to facilitate novel phage discovery.
Sirén,K., Millard,A., Petersen,B., Gilbert,M.T.P., Clokie,M.R.J. and Sicheritz-Pontén,T. (2020) Rapid discovery of novel prophages using biological feature engineering and machine learning. 10.1101/2020.08.09.243022. https://www.biorxiv.org/content/10.1101/2020.08.09.243022v1.abstract
conda create -y -n PhageBoost-env python=3.7
conda activate PhageBoost-env
pip install PhageBoost
PhageBoost -h
conda create -y -n PhageBoost-env python=3.7
conda activate PhageBoost-env
git clone git@github.com:ku-cbd/PhageBoost.git
cd PhageBoost/
python setup.py bdist_wheel
pip install --user .
PhageBoost -h
PhageBoost -h
PhageBoost -f example/data/NC_000907.fasta.gz -o results
There are basic notebook examples in the notebooks/
These notebooks provide a way how to bring your own genecalls to PhageBoost.
You can connect your PhageBoost kernel to your pre-existing Jupyter via ipykernel:
conda activate PhageBoost
pip install ipykernel
python -m ipykernel install --user --name PhageBoost --display-name "PhageBoost"