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--- |
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library_name: transformers |
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tags: |
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- CodonTransformer |
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- Computational Biology |
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- Machine Learning |
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- Bioinformatics |
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- Synthetic Biology |
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license: apache-2.0 |
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pipeline_tag: token-classification |
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--- |
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**Note this is the pretrained model. We recommend using the finetuned model available at https://huggingface.co/adibvafa/CodonTransformer** |
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**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. |
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## Use Case |
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**For an interactive demo, check out our [Google Colab Notebook.](https://adibvafa.github.io/CodonTransformer/GoogleColab)** |
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<br></br> |
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After installing CodonTransformer, you can use: |
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```python |
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import torch |
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from transformers import AutoTokenizer, BigBirdForMaskedLM |
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from CodonTransformer.CodonPrediction import predict_dna_sequence |
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from CodonTransformer.CodonJupyter import format_model_output |
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("adibvafa/CodonTransformer") |
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model = BigBirdForMaskedLM.from_pretrained("adibvafa/CodonTransformer").to(DEVICE) |
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# Set your input data |
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protein = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGG" |
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organism = "Escherichia coli general" |
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# Predict with CodonTransformer |
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output = predict_dna_sequence( |
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protein=protein, |
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organism=organism, |
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device=DEVICE, |
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tokenizer_object=tokenizer, |
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model_object=model, |
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attention_type="original_full", |
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) |
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print(format_model_output(output)) |
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``` |
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The output is: |
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<br> |
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```python |
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----------------------------- |
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| Organism | |
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----------------------------- |
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Escherichia coli general |
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----------------------------- |
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| Input Protein | |
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----------------------------- |
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MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGG |
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----------------------------- |
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| Processed Input | |
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----------------------------- |
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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 |
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----------------------------- |
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| Predicted DNA | |
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----------------------------- |
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ATGGCTTTATGGATGCGTCTGCTGCCGCTGCTGGCGCTGCTGGCGCTGTGGGGCCCGGACCCGGCGGCGGCGTTTGTGAATCAGCACCTGTGCGGCAGCCACCTGGTGGAAGCGCTGTATCTGGTGTGCGGTGAGCGCGGCTTCTTCTACACGCCCAAAACCCGCCGCGAAGCGGAAGATCTGCAGGTGGGCCAGGTGGAGCTGGGCGGCTAA |
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``` |
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## Additional Resources |
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- **Project Website** <br> |
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https://adibvafa.github.io/CodonTransformer/ |
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- **GitHub Repository** <br> |
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https://github.com/Adibvafa/CodonTransformer |
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- **Google Colab Demo** <br> |
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https://adibvafa.github.io/CodonTransformer/GoogleColab |
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- **PyPI Package** <br> |
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https://pypi.org/project/CodonTransformer/ |
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- **Paper** <br> |
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TBD |
