RNAGenesis is a compact yet powerful RNA foundation model that unifies sequence understanding, de novo RNA design, and 3D structure prediction through a latent diffusion framework.
-
📌 Inference-time optimization
Introduces test-time directed generation strategies—combining tree search and gradient-based model guidance—to steer RNA design toward desired structure and function. -
📊 State-of-the-art performance
Achieves top results in 11 of 13 tasks on the BEACON benchmark for RNA sequence understanding. -
🧬 Versatile RNA generation
Synthesizes diverse non-coding RNAs, including natural-like aptamers and structurally optimized CRISPR sgRNAs. -
🧪 Experimental validation
RNAGenesis-designed sgRNAs outperform wild-type scaffolds in gene knockout efficiency, with up to 2× improvement across CRISPR-Cas9, base editing, and prime editing platforms.
# Create and activate conda environment
conda env create -f environment.yml
conda activate rnagenesis# Create and activate conda environment
conda create -n rnagenesis python=3.8.13
conda activate rnagenesis
# Install dependencies
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2
pip install transformers==4.32.1 diffusers==0.25.0 accelerate==0.25.0 logomaker==0.8 biopython==1.83 sentencepiece==0.1.99 huggingface-hub==0.28.1 wandb==0.19.6 pytorch-lightning==2.1.3 torchmetrics==1.2.1 xgboost==1.5.2 omegaconf==2.3.0
conda install numpy=1.22.0 pandas=1.3.1 scipy=1.10.1 matplotlib=3.7.1 seaborn=0.13.2 scikit-learn=0.24.0 jupyterlab=2.3.2 ipython=8.3.0 ipykernel=6.13.0 openssl=1.1.1o zlib=1.2.11 ca-certificates=2021.10.8 setuptools=59.5.0 wheel=0.37.1
pip install rna-fm
conda install -c bioconda viennarna# Create checkpoints directory
mkdir -p checkpoints
mkdir -p configs
# Download model weights
wget https://zenodo.org/records/15203813/files/checkpoints.zip?download=1
wget https://zenodo.org/records/15203813/files/configs.zip?download=1
wget https://zenodo.org/records/15203813/files/progen2-base.zip?download=1
wget https://zenodo.org/records/15203813/files/progen2-small.zip?download=1
# Extract model weights
unzip checkpoints.zip -d checkpoints/
unzip configs.zip -d confiigs/
unzip progen2-base.zip -d models/autoencoder/decoder/checkpoints/progen2-base/
unzip progen2-small.zip -d models/autoencoder/decoder/checkpoints/progen2-small/
# Clean up
rm -f checkpoints.zip
rm -f configs.zip
rm -f progen2-base.zip
rm -f progen2-small.zip- Generation with RNAGensis:
# RNAGenesis python generate.py \ --batch_size 128 \ --batch_num 200 \ --eos_token "2" \ --do_sample \ --top_p 0.95 \ --top_k 0 \ --max_seq_len 37 \ --enc_dec_file "configs/rnagenesis/autoencoder" \ --dm_file "checkpoints/Aptamer/diffusion" \ # for aptamer generation --superfolder "generation_sequences" \ --mid_folder "RNAGenesis_Aptamer"
# RNAGenesis python generation.py \ --batch_size 128 \ --batch_num 200 \ --eos_token "2" \ --do_sample \ --top_p 0.95 \ --top_k 0 \ --max_seq_len 64 \ --enc_dec_file "configs/rnagenesis/autoencoder" \ --dm_file "checkpoints/sgRNA/diffusion" \ # for sgRNA generation --superfolder "generation_sequences" \ --mid_folder "RNAGenesis_sgRNA"
- Generation with Guidance RNAGenesis:
# guidance RNAGenesis python generation.py \ --batch_size 128 \ --batch_num 200 \ --eos_token "2" \ --do_sample \ --top_p 0.95 \ --top_k 0 \ --max_seq_len 64 \ --enc_dec_file "configs/rnagenesis/autoencoder" \ --dm_file "checkpoints/sgRNA/diffusion" \ # for sgRNA generation --guidance \ --target_class 0 \ --guidance_classifier_model_config "configs/rangenesis/classifier/mlp_160_32.yaml" \ --classifier_loss_type 'ce' \ --guidance_scale 50.0 \ --recurrence_step 1 \ --superfolder "generation_sequences" \ --mid_folder "Guid_sgRNA"
- Generation with Beam-Search RNAGensis:
# tree search RNAGenesis python generation.py \ --batch_size 128 \ --batch_num 200 \ --eta 1 \ --search_general \ --search_goal "similarity" \ --active_size 1 \ --branch_size 8 \ --eos_token "2" \ --do_sample \ --top_p 0.95 \ --top_k 0 \ --max_seq_len 37 \ --enc_dec_file "configs/rnagenesis/autoencoder" \ --dm_file "checkpoints/sgRNA/diffusion" \ # for sgRNA generation --superfolder "generation_sequences" \ --mid_folder "BS_sgRNA"
It takes around 5 hours to generate all the sequences on 1 A100 GPU.
If you find this work helpful, please cite our paper:
@article{zhang2024rna,
title={RNAGenesis: Foundation Model for Enhanced RNA Sequence Generation and Structural Insights},
author={Zhang, Zaixi and Chao, Linlin and Jin, Ruofan and Zhang, Yikun and Zhou, Guowei and Yang, Yujie and Yang, Yukang and Huang, Kaixuan and Yang, Qirong and Xu, Ziyao and Zhang, Xiaoming and Cong, Le and Wang, Mengdi},
journal={bioRxiv},
pages={2024--12},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}We thank the following open-source projects for their valuable contributions:
This project is licensed under the MIT License - see the LICENSE file for details.