PrimeNet: A Deep Learning Model for Prime Editing Efficiency Prediction

Introduction

PrimeNet is a deep learning model designed to predict Prime editing efficiency with high accuracy by integrating epigenetic factors such as chromatin accessibility and DNA methylation. Unlike existing models, PrimeNet leverages multi-scale convolution and attention mechanisms to extract diverse sequence features, achieving superior generalization across different conditions. Our results show that PrimeNet achieves a Spearman correlation coefficient of 0.94, outperforming existing models.

Model input

• Sequence: Wild Sequence and the Edited Sequence.
• Epigenetic information: chromatin accessibility and DNA methylation status. For chromatin accessibility features, a site is labeled "Y" if it is chromatin accessible and "N" otherwise. For methylation features, a site is labeled as "Y" if it is methylated and "N" if it is unmethylated.
• Functional area information: The positions of Protospacer, PAM, PBS, and RT in the sequence.

Data Encoding

We encode the input data as an image for deep learning processing:

Model output

• valid editing efficiency: The proportion of sequences that are successfully edited as intended, meaning the desired genetic modification is correctly introduced.
• unedited efficiency: The proportion of sequences that remain unchanged, indicating that Prime editing did not occur.
• erroneous editing efficiency: The proportion of sequences that were edited incorrectly, meaning unintended modifications occurred instead of the expected edit.

Installation

1. Clone the Repository

# Clone the repository
git clone https://github.com/bm2-lab/PrimeNet.git
cd PrimeNet

2. Install Dependencies

# Ensure you have Python 3.8+ installed, then run:
pip install numpy pandas torch scikit-learn scipy

File Descriptions

• PrimeNet.pth: The trained model weights file.
• model.py: A script that defines the model architecture and forward propagation logic.
• test.py: A script used for loading the model and evaluating it on test data.
• train_val.py: A script for training the model and validating it during training.
• data: The dataset.

License

This project is licensed under the Apache License. See LICENSE for details.

Contact

For questions or collaborations, please contact [email protected] or open an issue on GitHub.