PRIDICT2.0 v1.1 - Protocol Integration
🆕 What's New
- 📖 Comprehensive Protocol: Integration of our detailed Nature Protocols paper with step-by-step instructions
- 🚀 Enhanced Documentation: Updated README with protocol information and practical guidance
Overview
PRIDICT2.0 is an advanced version of the original PRIDICT model designed for predicting the efficiency of prime editing guide RNAs. This repository allows you to run the model locally. For details on advancements over the original model, refer to our published study (Mathis et al., Nature Biotechnology, 2024) and the initial BioRxiv preprint.
NEW: For comprehensive step-by-step instructions, including practical tips for high-throughput screening, see our detailed protocol (Mathis et al., Nature Protocols, 2025).
Complementary Models
- ePRIDICT: This model focuses on the influence of local chromatin context (K562) on prime editing efficiencies and is designed to complement PRIDICT2.0. Access GitHub Repository
- DeepPrime: This is a complementary model from Yu et al. 2023 providing pegRNA design efficiency predictions for edit types <= 3 bp. Access GitHub Repository
Resources
- 🆕 Protocol Paper: Mathis et al., Nature Protocols, 2025
- Supplementary Files: Access Here
- Web Application: For an online version of PRIDICT2.0, visit our webapp.
Contact
For questions or suggestions, please either:
- Email us at nicolas.mathis@pharma.uzh.ch
- Open a GitHub issue
Citation
If you use our tools in your research, please cite:
For using PRIDICT/PRIDICT2.0:
- Mathis et al., Nature Biotechnology, 2024 (PRIDICT2.0)
- Mathis & Allam et al., Nature Biotechnology, 2023 (PRIDICT)
🆕 For following our protocol:
- Mathis et al., Nature Protocols, 2025 (Protocol)