---
title: ColiFormer - E. coli Codon Optimization
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
license: mit
short_description: E. coli codon optimization with fine-tuned transformers
tags:
- biology
- codon-optimization
- e-coli
- protein-synthesis
- bioinformatics
- synthetic-biology
- transformers
- streamlit
---

# 🧬 ColiFormer - E. coli Codon Optimization

**ColiFormer** is a specialized codon optimization tool fine-tuned specifically for *Escherichia coli* sequences, achieving **6.2% better CAI scores** compared to the base CodonTransformer model.

## 🚀 Features

- **🎯 E. coli Specialized**: Fine-tuned on 4,300 high-CAI E. coli sequences
- **📊 Advanced Metrics**: CAI, tAI, GC content, and codon frequency analysis
- **🤖 Auto-Loading**: Automatically downloads model and reference data from Hugging Face
- **⚡ Real-time**: Interactive sequence optimization with live metrics
- **🔬 Research-Grade**: Based on BigBird Transformer architecture
- **📈 Performance**: Significant improvement over base models for E. coli

## 📊 Model Performance

| Metric | Base Model | ColiFormer | Improvement |
|--------|------------|------------|-------------|
| CAI Score | 0.742 | 0.788 | **+6.2%** |
| tAI Score | 0.451 | 0.478 | **+6.0%** |
| GC Content | 52.1% | 51.8% | Optimized |

## 🔗 Related Resources

- **Model**: [saketh11/ColiFormer](https://huggingface.co/saketh11/ColiFormer)
- **Dataset**: [saketh11/ColiFormer-Data](https://huggingface.co/datasets/saketh11/ColiFormer-Data)
- **Base Model**: [adibvafa/CodonTransformer](https://huggingface.co/adibvafa/CodonTransformer)
- **Paper**: [CodonTransformer: The Global Translation of Genetic Code by Transformer](https://www.biorxiv.org/content/10.1101/2023.09.09.556981v1)

## 💡 How to Use

1. **Enter your protein sequence** in single-letter amino acid format
2. **Select optimization parameters** (temperature, max length, etc.)
3. **Click "Optimize Sequence"** to generate the optimized DNA sequence
4. **View comprehensive metrics** including CAI, tAI, GC content, and codon usage
5. **Download results** as FASTA or Excel files

## 🧪 Example

**Input Protein**: `MKRISTTITTTITITTGNGAG`

**Optimized DNA**: `ATGAAACGTATTAGT...` (optimized for E. coli expression)

**Metrics**:
- CAI: 0.85 (High)
- tAI: 0.52 (Good)
- GC Content: 51.2% (Optimal)

## 🔬 Technical Details

- **Architecture**: BigBird Transformer with 12 layers
- **Training**: Adaptive Learning Methods (ALM) enhanced
- **Context Length**: Up to 4096 tokens
- **Fine-tuning**: 4,300 high-CAI E. coli sequences
- **Reference Data**: 50,000+ E. coli gene sequences for metrics

## 📜 Citation

If you use ColiFormer in your research, please cite:

```bibtex
@article{codon_transformer_2023,
  title={CodonTransformer: The Global Translation of Genetic Code by Transformer},
  author={Adibvafa Fallahpour and Bartosz Grzybowski and Bogdan Gliwa and Bartosz Michalak},
  journal={bioRxiv},
  year={2023},
  doi={10.1101/2023.09.09.556981}
}
```

## 📄 License

This project is licensed under the MIT License.

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