DeepCRISPR is a deep learning based prediction model for sgRNA on-target knockout efficacy and genome-wide off-target cleavage profile prediction.
This model is based on a carefully designed hybrid deep neural network for model training and prediction.
Current version focuses on conventional NGG-based sgRNA design for SpCas9 in human species, for it is widely used in related experiments.
Online version of DeepCRISPR is also maintained.
- python == 3.6
- tensorflow == 1.3.0
- sonnet == 1.9
- Digitalize sgRNA using the following sgRNA Coding Schema. Epigenetics features can be found in ENCODE.
- Load models from model directories (untar them first!) in
trained_models. - Perform prediction.
...
import tensorflow as tf
from deepcrispr import DCModel
# Digitalization
x_on_target = ... # [batch_size, 8, 1, 23]
x_sg_off_target = ... # [batch_size, 8, 1, 23]
x_ot_off_target = ... # [batch_size, 8, 1, 23]
# Loading model
sess = tf.InteractiveSession()
on_target_model_dir = '...'
off_target_model_dir = '...'
dcmodel = DCModel(sess, on_target_model_dir, off_target_model_dir)
# Prediction
predicted_on_target = dcmodel.ontar_predict(x_on_target)
predicted_off_target = dcmodel.offtar_predict(x_sg_off_target, x_ot_off_target)