Baskerville provides researchers with tools to:
- Train deep convolutional neural networks to predict regulatory activity along very long chromosome-scale DNA sequences
- Score variants according to their predicted influence on regulatory activity across the sequence and/or for specific genes.
- Annotate the specific nucleotides that drive regulatory element function.
Documentation page: https://calico.github.io/baskerville/index.html
git clone [email protected]:calico/baskerville.git
cd baskerville
pip install .
To set up the required environment variables:
cd baskerville
conda activate <conda_env>
./env_vars.sh
Note: Change the two lines of code at the top of './env_vars.sh' to the correct local paths.
Alternatively, the environment variables can be set manually:
export BASKERVILLE_DIR=/home/<user_path>/baskerville
export PATH=$BASKERVILLE_DIR/src/baskerville/scripts:$PATH
export PYTHONPATH=$BASKERVILLE_DIR/src/baskerville/scripts:$PYTHONPATH
export BASKERVILLE_CONDA=/home/<user>/anaconda3/etc/profile.d/conda.sh
Dave Kelley (codeowner)