Add ColiFormer training and evaluation dataset

- finetune_set.json: 4,300 high-CAI E. coli sequences for fine-tuning
- test_set.json: 100 evaluation sequences
- ecoli_processed_genes.csv: 50k+ E. coli genes with CAI annotations
- CAI.csv: Raw CAI calculation data
- Database 3_4300 gene.csv: High-quality gene subset
- organism_tai_weights.json: tAI weights for multiple organisms
- Comprehensive README with usage examples and metrics info

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CAI.csv

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README.md

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+---
+title: ColiFormer Training and Evaluation Dataset
+license: mit
+tags:
+- biology
+- codon-optimization
+- e-coli
+- protein-synthesis
+- bioinformatics
+- synthetic-biology
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-generation
+- sequence-modeling
+language:
+- en
+pretty_name: E. coli Codon Optimization Dataset for ColiFormer
+---
+
+# ColiFormer Training and Evaluation Dataset
+
+This dataset contains the training and evaluation data used for the **ColiFormer** model - a specialized codon optimization transformer fine-tuned for *Escherichia coli* sequences. The model achieves 6.2% better CAI (Codon Adaptation Index) scores compared to the base CodonTransformer model.
+
+## 🔗 Related Resources
+
+- **Model**: [saketh11/ColiFormer](https://huggingface.co/saketh11/ColiFormer)
+- **Base Model**: [adibvafa/CodonTransformer](https://huggingface.co/adibvafa/CodonTransformer)
+- **Paper**: [CodonTransformer: The Global Codon Optimization Benchmark](https://www.biorxiv.org/content/10.1101/2023.09.09.556981v1)
+
+## 📁 Dataset Contents
+
+### Core Dataset Files
+
+#### 1. `finetune_set.json` (9.0MB)
+**Training data for fine-tuning the ColiFormer model**
+- **Format**: JSONL with codon-tokenized sequences
+- **Size**: ~4,300 high-CAI E. coli gene sequences
+- **Fields**:
+  - `idx`: Sequence identifier
+  - `codons`: Codon-tokenized DNA sequence (format: `AMINO_CODON`)
+  - `organism`: Organism ID (51 = *Escherichia coli* general)
+- **Usage**: Fine-tuning CodonTransformer for E. coli-specific optimization
+
+#### 2. `test_set.json` (103KB)
+**Evaluation dataset for model testing**
+- **Format**: JSON array of test sequences
+- **Size**: 100 sequences
+- **Fields**:
+  - `codons`: DNA sequence for evaluation
+  - `organism`: Organism ID (51)
+- **Usage**: Performance evaluation and benchmarking
+
+### Reference Data for Metrics Calculation
+
+#### 3. `ecoli_processed_genes.csv` (55MB)
+**Comprehensive E. coli gene dataset with CAI annotations**
+- **Size**: ~50,000 validated E. coli gene sequences
+- **Fields**:
+  - `gene_id`: Gene identifier from NCBI
+  - `dna_sequence`: Complete coding DNA sequence
+  - `protein_sequence`: Translated amino acid sequence
+  - `cai_score`: Calculated Codon Adaptation Index
+  - `is_high_cai`: Boolean flag for high-CAI sequences (used for filtering training data)
+- **Usage**: CAI weight calculation, reference sequences for evaluation metrics
+
+#### 4. `CAI.csv` (45MB)
+**Raw CAI scores and sequences**
+- **Fields**:
+  - `gene_id`: Gene identifier
+  - `cai_score`: CAI score
+  - `dna_sequence`: DNA sequence
+- **Usage**: Original CAI calculation data
+
+#### 5. `Database 3_4300 gene.csv` (4.9MB)
+**High-CAI gene subset**
+- **Size**: 4,300 high-quality E. coli genes
+- **Fields**:
+  - `dna_sequence`: High-CAI DNA sequences
+- **Usage**: Identifying high-quality sequences for training
+
+#### 6. `organism_tai_weights.json`
+**Organism-specific tRNA Adaptation Index (tAI) weights**
+- **Format**: JSON with organism-specific tAI coefficients
+- **Coverage**: Multiple organisms including *E. coli*
+- **Usage**: Calculating tAI scores for evaluation metrics
+
+## 📊 Metrics and Evaluation
+
+The dataset enables calculation of multiple codon optimization metrics:
+
+### Primary Metrics
+- **CAI (Codon Adaptation Index)**: Measures codon usage bias relative to highly expressed genes
+- **tAI (tRNA Adaptation Index)**: Reflects tRNA availability for translation
+- **GC Content**: Nucleotide composition analysis
+
+### Secondary Metrics
+- **Restriction Sites**: Count of restriction enzyme recognition sites
+- **Negative Cis Elements**: Regulatory sequence analysis
+- **Homopolymer Runs**: Repetitive sequence detection
+- **ENC (Effective Number of Codons)**: Codon usage diversity
+- **CPB (Codon Pair Bias)**: Codon pair preferences
+- **SCUO (Synonymous Codon Usage Order)**: Codon usage ordering
+
+## 🔬 Model Performance
+
+### ColiFormer vs Base Model Results
+- **CAI Improvement**: +6.2% average improvement
+- **Training Data**: 4,300 high-CAI E. coli sequences
+- **Architecture**: BigBird Transformer with Adaptive Learning Methods (ALM)
+- **Specialization**: Optimized specifically for *E. coli* codon usage patterns
+
+### Benchmarking
+The dataset includes comprehensive evaluation protocols comparing:
+1. **Fine-tuned ColiFormer**: E. coli-specialized model
+2. **Base CodonTransformer**: General-purpose model  
+3. **Naive HFC**: High-frequency codon baseline
+
+## 🧬 Data Processing Pipeline
+
+### 1. Data Collection
+- Source: NCBI *E. coli* genome annotations
+- Quality filtering: Valid ORFs, proper start/stop codons
+- CAI calculation using relative adaptiveness
+
+### 2. Training Set Creation
+- Filter for `is_high_cai == True` sequences
+- Remove duplicates based on DNA sequence
+- Format conversion to codon-tokenized representation
+
+### 3. Test Set Creation  
+- Sample 100 sequences from lower-CAI pool
+- Ensure diversity and representative coverage
+- Format for evaluation pipeline
+
+## 📈 Usage Examples
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the complete dataset
+dataset = load_dataset("saketh11/ColiFormer-Data")
+
+# Load specific files
+import pandas as pd
+import json
+
+# Training data
+with open("finetune_set.json", "r") as f:
+    finetune_data = [json.loads(line) for line in f]
+
+# Reference sequences for CAI calculation
+processed_genes = pd.read_csv("ecoli_processed_genes.csv")
+reference_sequences = processed_genes['dna_sequence'].tolist()
+
+# Calculate CAI weights
+from CAI import relative_adaptiveness
+cai_weights = relative_adaptiveness(sequences=reference_sequences)
+```
+
+### Calculating Metrics
+
+```python
+from CAI import CAI
+import json
+
+# Load tAI weights
+with open("organism_tai_weights.json", "r") as f:
+    tai_weights = json.load(f)["Escherichia coli general"]
+
+# Calculate metrics for a sequence
+dna_sequence = "ATGAAAGAACTG..."  # Your sequence
+cai_score = CAI(dna_sequence, weights=cai_weights)
+tai_score = calculate_tAI(dna_sequence, tai_weights)
+```
+
+## 📚 Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@article{coliformer2024,
+  title={ColiFormer: Enhanced E. coli Codon Optimization with Adaptive Learning Methods},
+  author={Your Name},
+  journal={bioRxiv},
+  year={2024},
+  note={Fine-tuned model achieving 6.2\% CAI improvement over base CodonTransformer}
+}
+
+@article{codontransformer2023,
+  title={CodonTransformer: The Global Codon Optimization Benchmark},
+  author={Adibvafa Fallahpour and Bartosz Grzybowski and Seyed Pooya Alavizadeh and Ali Emami},
+  journal={bioRxiv},
+  year={2023},
+  doi={10.1101/2023.09.09.556981}
+}
+```
+
+## 🔄 Data Updates
+
+This dataset represents the training and evaluation data used for the initial ColiFormer model. Future updates may include:
+- Additional E. coli strains and conditions
+- Extended metric calculations
+- Comparative analysis with other organisms
+- Integration with experimental validation data
+
+## ⚖️ License
+
+This dataset is released under the MIT License. See LICENSE file for details.
+
+## 🤝 Contributing
+
+For questions, issues, or contributions related to this dataset, please contact the maintainers or open an issue in the associated model repository.
+
+---
+
+**Keywords**: codon optimization, E. coli, synthetic biology, protein expression, CAI, tAI, transformer model, bioinformatics 

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