DeepCRISPR

Introduction

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.

Requirement

• python == 3.6
• tensorflow == 1.3.0
• sonnet == 1.9

Docker image

docker pull michaelchuai/deepcrispr:latest

Note:

1. Using the command above to attain DeepCRISPR image;
2. The path of DeepCRISPR program and trained models in the image is /root/DeepCRISPR.

Usage

1. Digitalize sgRNA using the following sgRNA Coding Schema. Epigenetics features can be found in ENCODE.
2. Load models from model directories (untar them first!) in trained_models.
3. Perform prediction.

On-target prediction

Digitalization

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]

Loading Model

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

Prediction

predicted_on_target = dcmodel.ontar_predict(x_on_target)

Off-target prediction

Digitalization

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]

Loading Model

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

Prediction

predicted_off_target = dcmodel.offtar_predict(x_sg_off_target, x_ot_off_target)

Citation

Guohui Chuai, Qi Liu et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. 2018 (Manuscript submitted)

Contacts

18alexanderm117@tongji.edu.cn or qiliu@tongji.edu.cn