C-RNNCrispr is a framework for CRISPR/Cas9 single guide RNA (sgRNA) on-target activity prediction.
- Ubuntu 16.04
- Anaconda 3-5.2.0
- Python packages:
numpy 1.16.4
pandas 0.23.0
scikit-learn 0.19.1
scipy 1.1.0 - Keras 2.1.0
- Tensorflow and dependencies:
Tensorflow 1.4.0
CUDA 8.0 (for GPU use)
cuDNN 6.0 (for GPU use)
Ubuntu 16.04 download from https://www.ubuntu.com/download/desktop
Download Anaconda 3-5.2.0 tarball on https://www.anaconda.com/distribution/#download-section
pip install tensorflow-gpu==1.4.0 (for GPU use)
pip install tensorflow==1.4.0 (for CPU use)
Download CUDA tarball on https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
Download cuDNN tarball on https://developer.nvidia.com/cudnn
- data/input_example.csv: The testing examples with sgRNA sequence and corresponding epigenetic features and label indicating the on-target cleavage efficacy
- weights/C_RNNCrispr_weights.h5: The well-trained weights for our model
- C_RNNCrispr_test.py: The Python code, it can be ran to reproduce our results
- result/output_example.csv: The prediction results
The input_example.csv can replaced or modified to include gRNA sequence and four epigenetic features of interest
python C_RNNCrispr_test.py