README.md

---
license: mit
task_categories:
- text-classification
- sequence-modeling
language:
- en
tags:
- genomics
- variant-effect-prediction
- transcription-factor-binding
- regulatory-genomics
- bioinformatics
size_categories:
- 10K<n<100K
---

# TFBS Benchmark Dataset

A benchmark dataset for evaluating variant effect prediction models on transcription factor binding site (TFBS) disruption tasks.

## Dataset Description

This dataset contains 90,758 variants in transcription factor binding sites with comprehensive annotations for benchmarking variant effect prediction models, particularly the LOL-EVE model.

### Dataset Summary

- **Total variants**: 90,758
- **Species**: Primarily human (homo_sapiens)
- **Genes**: Multiple genes with TFBS variants
- **Transcription factors**: Various TFs including CAMTA1, CAMTA2, CLOCK, E2F6, EBF3, ETV6, HAP1, HIC2, HIF1A, HSF1, KLF15, KLF7, MAX, MNT, and others
- **Sequence context**: 500bp promoter sequences centered on variants

## Dataset Structure

### Features

| Feature | Type | Description |
|---------|------|-------------|
| `variant_id` | string | Unique identifier for each variant |
| `chromosome` | string | Chromosome (e.g., chr1) |
| `position` | int64 | Genomic position |
| `ref` | string | Reference allele |
| `alt` | string | Alternative allele |
| `gene` | string | Gene symbol |
| `species` | string | Species name (e.g., homo_sapiens) |
| `tf` | string | Transcription factor name |
| `wt_seq` | string | Wild-type sequence (500bp) |
| `var_seq` | string | Variant sequence (500bp) |
| `mammalian_constraint` | float32 | Mammalian constraint score |
| `expression_variability` | float32 | Expression variability score |
| `distance_tss` | int64 | Distance to transcription start site |

### Data Splits

The dataset contains a single split with all 90,758 variants.

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("cshearer/LOL-EVE-TFBS-Benchmark")

# Access the data
train_data = dataset["train"]
print(f"Dataset size: {len(train_data)}")
print(f"Features: {list(train_data.features.keys())}")

# Example usage
example = train_data[0]
print(f"Variant ID: {example['variant_id']}")
print(f"Gene: {example['gene']}")
print(f"TF: {example['tf']}")
print(f"Position: {example['chromosome']}:{example['position']}")
```

### Example Data

```python
# Example variant
{
    'variant_id': 'chr1_1231946_CCGCCAACG_C',
    'chromosome': 'chr1',
    'position': 1231946,
    'ref': 'CCGCCAACG',
    'alt': 'C',
    'gene': 'b3galt6',
    'species': 'homo_sapiens',
    'tf': 'CAMTA1',
    'wt_seq': 'GAGCCAGACATCAAGGGCTCCACACAGCCGACTTCACATCTCCAAATCCTACTAACTGGGGATGAGGGTCCACGCGGTTCAGAAGCGGAAGCGCAGGCGCAGGGAAGCGGGGCAGCTTGTCCAAGGTCGCCTCGCCGATAAACGCGAGTCCAACCAGACCCCTTGGGCCTCCGTTTCCCGGTGGCATTCGTAGGTTTTGGCCAGTAGGAGACCAGACGTGCCGGCGGCCGGGGAGGCCAGCGTCGTCGGCCTGTCCCTGCCCCCGGGAACCCCGGGAGCCCCGGTGGCGGCGGAGTCTCGCCAGGGCTCAAGGCCGAGCGGACGGACGATGCCCCAGCCCAAGGCGGGAGGCGGCGGCGGCCTCCAGACCCGCCCTCGCCGTCCGGCCGGCGTACACTTGGCCCCGCGGCCTGCAGCGGCCGTCCCGGGCCCCTCACTCACCGGTCTGCCTCCCCGCGCTCGGGATCCGAGGACCGGAGCGAAGCGTCAGTGACGCCGCCAACGGGCCCGGATCAGGCCACTGCCATCTTTCTTGCGGGCGGGGGCGGTGCGAACGGGCGCGACCTCACGGAGGGGACGCCGGCGCCACCATCTCTCCTCCGGGCGGAAGCGGTCGCGGGGCCGCTCCGAGGTTGACCAATGACAAGGGTGCCCGAGGCCACGTGACGGCCGCCGATTGGCCGCCGGCCTCCGAGCGCCCCGGGGCTCGGCGTCTGCGGAAGGCCCCGGCGCGCTCCCAGGAGCGCCGTGCGCACGCGCACCGCCCCGAGCCGGCGGCGCCTGCGCA',
    'var_seq': 'GAGCCAGACATCAAGGGCTCCACACAGCCGACTTCACATCTCCAAATCCTACTAACTGGGGATGAGGGTCCACGCGGTTCAGAAGCGGAAGCGCAGGCGCAGGGAAGCGGGGCAGCTTGTCCAAGGTCGCCTCGCCGATAAACGCGAGTCCAACCAGACCCCTTGGGCCTCCGTTTCCCGGTGGCATTCGTAGGTTTTGGCCAGTAGGAGACCAGACGTGCCGGCGGCCGGGGAGGCCAGCGTCGTCGGCCTGTCCCTGCCCCCGGGAACCCCGGGAGCCCCGGTGGCGGCGGAGTCTCGCCAGGGCTCAAGGCCGAGCGGACGGACGATGCCCCAGCCCAAGGCGGGAGGCGGCGGCGGCCTCCAGACCCGCCCTCGCCGTCCGGCCGGCGTACACTTGGCCCCGCGGCCTGCAGCGGCCGTCCCGGGCCCCTCACTCACCGGTCTGCCTCCCCGCGCTCGGGATCCGAGGACCGGAGCGAAGCGTCAGTGACGCGGCCCGGATCAGGCCACTGCCATCTTTCTTGCGGGCGGGGGCGGTGCGAACGGGCGCGACCTCACGGAGGGGACGCCGGCGCCACCATCTCTCCTCCGGGCGGAAGCGGTCGCGGGGCCGCTCCGAGGTTGACCAATGACAAGGGTGCCCGAGGCCACGTGACGGCCGCCGATTGGCCGCCGGCCTCCGAGCGCCCCGGGGCTCGGCGTCTGCGGAAGGCCCCGGCGCGCTCCCAGGAGCGCCGTGCGCACGCGCACCGCCCCGAGCCGGCGGCGCCTGCGCACCTGCGCA',
    'mammalian_constraint': 0.9789,
    'expression_variability': 0.3259307772539296,
    'distance_tss': 708
}
```

## Applications

This dataset is designed for:

1. **Variant Effect Prediction**: Benchmarking models that predict the functional impact of genetic variants
2. **Transcription Factor Binding**: Evaluating models that predict TF binding site disruption
3. **Regulatory Genomics**: Studying the impact of variants on gene regulation
4. **Model Comparison**: Comparing different variant effect prediction approaches

## Data Collection

### Source Data

The dataset is derived from variants in transcription factor binding sites with the following characteristics:

- **Genomic regions**: Promoter regions (500bp sequences)
- **Variant types**: Substitutions, insertions, and deletions
- **Annotation sources**: Mammalian constraint scores, expression variability, and TSS distance

### Preprocessing

- Variants were filtered to include only those in TFBS regions
- Sequences were extracted as 500bp windows centered on variants
- Functional annotations were added from external databases
- Data was standardized and validated for consistency

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{tfbs_benchmark_2024,
  title={TFBS Benchmark Dataset for Variant Effect Prediction},
  author={Marks Lab},
  year={2024},
  url={https://huggingface.co/datasets/cshearer/LOL-EVE-TFBS-Benchmark},
  license={MIT}
}
```

## License

This dataset is released under the MIT License. See the LICENSE file for details.

## Contact

For questions or issues related to this dataset, please contact the Marks Lab or open an issue on the dataset repository.

## Related Datasets

- [LOL-EVE Model](https://huggingface.co/Marks-lab/LOL-EVE)
- [eQTL Causal Dataset](https://huggingface.co/datasets/Marks-lab/LOL-EVE-eQTL)
- [Ultra Rare Variants Dataset](https://huggingface.co/datasets/Marks-lab/LOL-EVE-Ultra-Rare)

## Dataset Statistics

- **Total variants**: 90,758
- **Unique genes**: Multiple
- **Unique TFs**: 15+ transcription factors
- **Sequence length**: 500bp
- **Species**: Primarily human (homo_sapiens)
- **Variant types**: Substitutions, insertions, deletions
- **Genomic coverage**: Multiple chromosomes