CodonTransformer is the ultimate tool for codon optimization, transforming protein sequences into optimized DNA sequences specific for your target organisms. Whether you are a researcher or a practitioner in genetic engineering, CodonTransformer provides a comprehensive suite of features to facilitate your work. By leveraging the Transformer architecture and a user-friendly Jupyter notebook, it reduces the complexity of codon optimization, saving you time and effort.
Authors
Adibvafa Fallahpour1,2*, Vincent Gureghian3*, Guillaume J. Filion2‡, Ariel B. Lindner3‡, Amir Pandi3‡
1 Vector Institute for Artificial Intelligence, Toronto ON, Canada
2 University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada
3 Université Paris Cité, INSERM U1284, Center for Research and Interdisciplinarity, F-75006 Paris, France
* These authors contributed equally to this work.
‡ To whom correspondence should be addressed:
guillaume.filion@utoronto.ca, ariel.lindner@inserm.fr, amir.pandi@cri-paris.org
Use Case
For an interactive demo, check out our Google Colab Notebook. After installing CodonTransformer, you can use:
import torch
from transformers import AutoTokenizer, BigBirdForMaskedLM
from CodonTransformer.CodonPrediction import predict_dna_sequence
from CodonTransformer.CodonJupyter import format_model_output
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("adibvafa/CodonTransformer")
model = BigBirdForMaskedLM.from_pretrained("adibvafa/CodonTransformer").to(device)
# Set your input data
protein = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGG"
organism = "Escherichia coli general"
# Predict with CodonTransformer
output = predict_dna_sequence(
protein=protein,
organism=organism,
device=device,
tokenizer=tokenizer,
model=model,
attention_type="original_full",
deterministic=True
)
print(format_model_output(output))
The output is:
-----------------------------
| Organism |
-----------------------------
Escherichia coli general
-----------------------------
| Input Protein |
-----------------------------
MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGG
-----------------------------
| Processed Input |
-----------------------------
M_UNK A_UNK L_UNK W_UNK M_UNK R_UNK L_UNK L_UNK P_UNK L_UNK L_UNK A_UNK L_UNK L_UNK A_UNK L_UNK W_UNK G_UNK P_UNK D_UNK P_UNK A_UNK A_UNK A_UNK F_UNK V_UNK N_UNK Q_UNK H_UNK L_UNK C_UNK G_UNK S_UNK H_UNK L_UNK V_UNK E_UNK A_UNK L_UNK Y_UNK L_UNK V_UNK C_UNK G_UNK E_UNK R_UNK G_UNK F_UNK F_UNK Y_UNK T_UNK P_UNK K_UNK T_UNK R_UNK R_UNK E_UNK A_UNK E_UNK D_UNK L_UNK Q_UNK V_UNK G_UNK Q_UNK V_UNK E_UNK L_UNK G_UNK G_UNK __UNK
-----------------------------
| Predicted DNA |
-----------------------------
ATGGCTTTATGGATGCGTCTGCTGCCGCTGCTGGCGCTGCTGGCGCTGTGGGGCCCGGACCCGGCGGCGGCGTTTGTGAATCAGCACCTGTGCGGCAGCCACCTGGTGGAAGCGCTGTATCTGGTGTGCGGTGAGCGCGGCTTCTTCTACACGCCCAAAACCCGCCGCGAAGCGGAAGATCTGCAGGTGGGCCAGGTGGAGCTGGGCGGCTAA
Additional Resources
-
Project Website https://adibvafa.github.io/CodonTransformer/
-
GitHub Repository https://github.com/Adibvafa/CodonTransformer
-
Google Colab Demo https://adibvafa.github.io/CodonTransformer/GoogleColab
-
PyPI Package https://pypi.org/project/CodonTransformer/
Citation
@article{Fallahpour_Gureghian_Filion_Lindner_Pandi_2025,
title={CodonTransformer: a multispecies codon optimizer using context-aware neural networks},
volume={16},
ISSN={2041-1723},
url={https://www.nature.com/articles/s41467-025-58588-7},
DOI={10.1038/s41467-025-58588-7},
number={1},
journal={Nature Communications},
author={Fallahpour, Adibvafa and Gureghian, Vincent and Filion, Guillaume J. and Lindner, Ariel B. and Pandi, Amir},
year={2025},
month=apr,
pages={3205},
language={en}
}