torinriley/CRISPR-Efficiency

CRISPR Efficiency Predictor

A deep learning model for predicting CRISPR-Cas9 editing efficiency based on DNA sequences and epigenetic features. This model integrates sequence data and epigenetic signals to provide highly accurate predictions of CRISPR editing efficiency.

Model Details

• —Model Type: Convolutional Neural Network (CNN)
• —Input Features:
• —DNA Sequence: 23-base target sequence, one-hot encoded.
• —Epigenetic Features:
• —CTCF (Transcription factor binding sites)
• —DNase (Chromatin accessibility)
• —H3K4me3 (Histone modification marker)
• —RRBS (Methylation marker)
• —Output: A single efficiency score indicating the likelihood of successful CRISPR editing for the given input.

Training and Evaluation

Training Details

• —Dataset: DeepCRISPR
• —Citation: Guohui Chuai, Qi Liu et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. 2018 (Manuscript submitted).
• —Framework: TensorFlow/Keras
• —Optimizer: Adam
• —Loss Function: Mean Squared Error (MSE)

Evaluation Metrics