The code were tesed on Linux and Mac OS systems. The required software/packages are:
- python==3.10.8
- matplotlib==3.7.0
- logging==0.5.1.2
- pkbar==0.5
- torch==1.13.1
- numpy==1.23.5
- pandas==1.4.3
- Bio==1.78
- sklearn==1.1.1
It is worth noting that when the computing environment(e.g, the version of tensorflow or biopython) changes, the prediction results might change slightly, but the main conclusion won't be affected.
conda create -n pytorch python=3.10.8 ipykernel matplotlib=3.7.0 logging=0.5.1.2 pkbar=0.5 torch=1.13.1 numpy=1.23.5 pandas=1.4.3 Bio=1.78 sklearn=1.1.1
ipython kernel install --user --name crispr --display-name "Python3(pytorch)"Installation time depends on your own network environment.
ABEdeepoff.ipynb provides the code for training model in your own computing environment for ABE.
CBEdeepoff.ipynb provides the code for training model in your own computing environment for CBE.
data/ABEdeepoff.txt experimental edit efficiency data for ABE. It can be used to train the model.
data/CBEdeepoff.txt experimental edit efficiency data for CBE. It can be used to train the model.
data/ABE_Off_endo.txt experimental edit efficiency data from independent publication dataset. It can be used to test the model.
data/CBE_Off_endo.txt experimental edit efficiency data from independent publication dataset. It can be used to test the model.
model/ABEdeepoff.pt ABEdeepoff model parameters used in online webserver.
model/CBEdeepoff.pt CBEdeepoff model parameters used in online webserver.
BEdeepoff.py Local version of ABEdeepoff and CBEdeepoff model.
usage: BEdeepoff.py [-h] [-i INPUT_FILE] [-o OUTPUT_FILE] [-t {ABE,CBE}]
Local version of ABEdeepoff and CBEdeepoff.
options:
-h, --help show this help message and exit
-i INPUT_FILE, --input-file INPUT_FILE
Input file include gRNA and offtarget sequences (tab-delimited).
-o OUTPUT_FILE, --output-file OUTPUT_FILE
Output table file name.
-t {ABE,CBE}, --editor-type {ABE,CBE}
Base editor type.
# demo
python3 BEdeepoff.py -i input.txt -o output.txt -t ABE