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---
library_name: transformers
tags:
- CodonTransformer
- Computational Biology
- Machine Learning
- Bioinformatics
- Synthetic Biology
license: apache-2.0
pipeline_tag: token-classification
---
**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.
<br>
**This is the pretrained model, for best results please use the [finetuned model](https://huggingface.co/adibvafa/CodonTransformer)**.
## Authors
Adibvafa Fallahpour<sup>1,2</sup>\*, Vincent Gureghian<sup>3</sup>\*, Guillaume J. Filion<sup>2</sup>‡, Ariel B. Lindner<sup>3</sup>‡, Amir Pandi<sup>3</sup>‡
<sup>1</sup> Vector Institute for Artificial Intelligence, Toronto ON, Canada
<sup>2</sup> University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada
<sup>3</sup> 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: <br>
guillaume.filion@utoronto.ca, ariel.lindner@inserm.fr, amir.pandi@cri-paris.org
<br>
## Use Case
**For a guide on finetuning CodonTransformer, check out our [GitHub.](https://github.com/Adibvafa/CodonTransformer/tree/main?tab=readme-ov-file#finetuning-codontransformer)**
<br>**For an interactive demo, check out our [Google Colab Notebook.](https://adibvafa.github.io/CodonTransformer/GoogleColab)**
<br></br>
After installing CodonTransformer, you can use:
```python
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-base").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",
)
print(format_model_output(output))
```
The output is:
<br>
```python
-----------------------------
| 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** <br>
https://adibvafa.github.io/CodonTransformer/
- **GitHub Repository** <br>
https://github.com/Adibvafa/CodonTransformer
- **Google Colab Demo** <br>
https://adibvafa.github.io/CodonTransformer/GoogleColab
- **PyPI Package** <br>
https://pypi.org/project/CodonTransformer/
- **Paper** <br>
https://www.biorxiv.org/content/10.1101/2024.09.13.612903 |