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.
- python == 3.6
- tensorflow == 1.3.0
- sonnet == 1.9
docker pull michaelchuai/deepcrispr:latestNote:
- Using the command above to attain DeepCRISPR image;
- The path of DeepCRISPR program and trained models in the image is
/root/DeepCRISPR.
- 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.
Choose 4 channels for sequence-only prediction or 8 channels for full-featured prediction, according to the sgRNA Coding Schema above.
import tensorflow as tf
from deepcrispr import DCModelOntar
seq_feature_only = False
channels = 4 if seq_feature_only else 8
x_on_target = ... # [batch_size, channels, 1, 23]sess = tf.InteractiveSession()
on_target_model_dir = '<model path>'
# using regression model, otherwise classification model
is_reg = True
# using sequences feature only, otherwise sequences feature + selected epigenetic features
seq_feature_only = False
dcmodel = DCModelOntar(sess, on_target_model_dir, is_reg, seq_feature_only)| Model file name | Description |
|---|---|
| ontar_ptaug_cnn.tar.gz | CNN-based on-target classification model with pre-training and data augmentation |
| ontar_pt_cnn_reg.tar.gz | CNN-based on-target regression model with pre-training and data augmentation |
| ontar_cnn_reg_seq.tar.gz | Sequence feature-only CNN-based on-target regression model with data augmentation |
predicted_on_target = dcmodel.ontar_predict(x_on_target)Off-target prediction supports full-featured prediction only.
import tensorflow as tf
from deepcrispr import DCModelOfftar
channels = 8
x_on_target = ... # [batch_size, channels, 1, 23]
x_sg_off_target = ... # [batch_size, channels, 1, 23]
x_ot_off_target = ... # [batch_size, channels, 1, 23]sess = tf.InteractiveSession()
off_target_model_dir = '<model path>'
# using regression model, otherwise classification model
is_reg = True
dcmodel = DCModelOfftar(sess, off_target_model_dir, is_reg)| Model file name | Description |
|---|---|
| offtar_pt_cnn.tar.gz | CNN-based off-target classification model with pre-training |
| ontar_pt_cnn_reg.tar.gz | CNN-based off-target regression model with pre-training |
predicted_off_target = dcmodel.offtar_predict(x_sg_off_target, x_ot_off_target)Guohui Chuai, Qi Liu et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. 2018 (Manuscript submitted)
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