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C-RNNCrispr: A deep learning framework for predicting CRISPR/Cas9 single guide RNA on-target activity

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C-RNNCrispr

Overview

C-RNNCrispr is a framework for CRISPR/Cas9 single guide RNA (sgRNA) on-target activity prediction.

Pre-requisite:

Installation guide

Operation system

Ubuntu 16.04 download from https://www.ubuntu.com/download/desktop

Python and packages

Download Anaconda 3-5.2.0 tarball on https://www.anaconda.com/distribution/#download-section

Tensorflow installation:

pip install tensorflow-gpu==1.4.0 (for GPU use)
pip install tensorflow==1.4.0 (for CPU use)

CUDA toolkit 8.0 (for GPU use)

Download CUDA tarball on https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run

cuDNN 6.1.10 (for GPU use)

Download cuDNN tarball on https://developer.nvidia.com/cudnn

Content

  • 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

Note:

The input_example.csv can replaced or modified to include gRNA sequence and four epigenetic features of interest

Testing C-RNNCrispr with test set

python C_RNNCrispr_test.py

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C-RNNCrispr: A deep learning framework for predicting CRISPR/Cas9 single guide RNA on-target activity

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