README.md

---
license: mit
pretty_name: DNA Benchmarks
language:
  - en
task_categories:
  - text-classification
  - feature-extraction
  - other
tags:
  - dna
  - genomics
  - genome
  - benchmark
  - genomic-benchmark
  - sequence-classification
  - regulatory-genomics
  - eqtl
  - promoter-prediction
  - enhancer-prediction
  - splice-site-prediction
  - chromatin-profile-prediction
  - nucleotide-transformer
  - visualdna
  - opticaldna
size_categories:
  - n<1K
  - 1K<n<10K
  - 10K<n<100K
  - 100K<n<1M
  - 1M<n<10M
  - 10M<n<100M
---

# DNA Benchmarks

## Dataset Description

**DNA Benchmarks** is a collection of genomic datasets organized for benchmarking DNA foundation models, genomic representation learning methods, and multimodal genomic learning frameworks.

The collection covers a wide range of sequence scales, from short regulatory sequences of a few hundred base pairs to genome-scale tiling datasets derived from whole genomes.

This repository is designed as a general-purpose benchmark hub rather than a dataset tied to a single model. It contains both original downloaded files and standardized raw files that can be used by sequence-based models, visual genomic representation models, and multimodal genomic models.

OpticalDNA uses selected datasets from this repository, but this dataset collection itself is model-agnostic and can be used independently of OpticalDNA.

This repository contains both:

1. **Official raw data** downloaded from the original public sources without additional processing.
2. **VisualDNA-compatible raw data** converted into a unified format that can be directly used by VisualDNA-based pipelines and downstream genomic modeling frameworks.

## Repository Structure

The repository contains two core directories:

```text
dna_benchmarks/
├── official_data/
└── raw_data/
```

### `official_data/`

The `official_data` directory stores files downloaded from official or original public sources without additional processing. These files are preserved to support data provenance and reproducibility.

Current structure:

```text
official_data/
├── data_long_range_dna/
│   └── eQTL.zip
└── genomic_benchmarks/
    ├── README.md
    ├── demo_coding_vs_intergenomic_seqs.zip
    ├── demo_human_or_worm.zip
    ├── drosophila_enhancers_stark.zip
    ├── dummy_mouse_enhancers_ensembl.zip
    ├── human_enhancers_cohn.zip
    ├── human_enhancers_ensembl.zip
    ├── human_ensembl_regulatory.zip
    ├── human_nontata_promoters.zip
    └── human_ocr_ensembl.zip
```

### `raw_data/`

The `raw_data` directory stores datasets in a format supported by VisualDNA-style data processing.

A dataset in this directory may be:

1. converted from the corresponding files in `official_data`, or
2. directly uploaded if it already follows a VisualDNA-supported raw format.

The standard layout is:

```text
raw_data/
└── <dataset_name>/
    └── raw/
        ├── <dataset_name>.csv
        └── statistic.txt
```

or, for larger datasets:

```text
raw_data/
└── <dataset_name>/
    └── raw/
        ├── <dataset_name>.parquet
        └── statistic.txt
```

Each `statistic.txt` file records basic dataset information, such as the number of samples, available columns, sequence statistics, label distribution, and split statistics when available.

## Dataset Groups

### 1. Genome-scale pretraining and tiling datasets

These datasets are designed for large-scale genome representation learning and pretraining.

```text
raw_data/
├── RiceSuperPIRdb-PRETRAIN_GENOME_TILING/
│   └── NIP-T2T_w2048_o1920_SeqCase.UPPER/
│       └── raw/
│           ├── NIP-T2T_w2048_o1920_SeqCase.UPPER.parquet
│           └── statistic.txt
└── hg38-2048/
    └── raw/
        ├── hg38-2048.parquet
        └── statistic.txt
```

These datasets can be used for:

- genome-scale pretraining
- masked sequence modeling
- long-context genomic sequence modeling
- cross-species genomic representation learning
- visual or multimodal genomic modeling

### 2. Long-range DNA / eQTL datasets

The repository includes long-range DNA datasets for evaluating models on regulatory signals that may require extended genomic context.

```text
raw_data/
└── data_long_range_dna/
    └── eqtl.zip
```

The `eqtl.zip` archive contains nine tissue-specific datasets:

```text
eqtl/
├── Adipose_Subcutaneous/
│   └── raw/
│       ├── Adipose_Subcutaneous.csv
│       └── statistic.txt
├── Artery_Tibial/
│   └── raw/
│       ├── Artery_Tibial.csv
│       └── statistic.txt
├── Cells_Cultured_fibroblasts/
│   └── raw/
│       ├── Cells_Cultured_fibroblasts.csv
│       └── statistic.txt
├── Muscle_Skeletal/
│   └── raw/
│       ├── Muscle_Skeletal.csv
│       └── statistic.txt
├── Nerve_Tibial/
│   └── raw/
│       ├── Nerve_Tibial.csv
│       └── statistic.txt
├── Skin_Not_Sun_Exposed_Suprapubic/
│   └── raw/
│       ├── Skin_Not_Sun_Exposed_Suprapubic.csv
│       └── statistic.txt
├── Skin_Sun_Exposed_Lower_leg/
│   └── raw/
│       ├── Skin_Sun_Exposed_Lower_leg.csv
│       └── statistic.txt
├── Thyroid/
│   └── raw/
│       ├── Thyroid.csv
│       └── statistic.txt
└── Whole_Blood/
    └── raw/
        ├── Whole_Blood.csv
        └── statistic.txt
```

These datasets are suitable for:

- eQTL prediction
- long-range regulatory modeling
- tissue-specific regulatory sequence prediction
- long-context genomic benchmark evaluation

### 3. Genomic Benchmarks datasets

The `genomic_benchmarks` group contains standard DNA sequence classification benchmarks converted into the unified raw-data layout.

```text
raw_data/
└── genomic_benchmarks/
    ├── demo_coding_vs_intergenomic_seqs/
    ├── demo_human_or_worm/
    ├── drosophila_enhancers_stark/
    ├── dummy_mouse_enhancers_ensembl/
    ├── human_enhancers_cohn/
    ├── human_enhancers_ensembl/
    ├── human_ensembl_regulatory/
    ├── human_nontata_promoters/
    └── human_ocr_ensembl/
```

Each dataset follows:

```text
<dataset_name>/
└── raw/
    ├── <dataset_name>.csv
    └── statistic.txt
```

These datasets cover tasks such as:

- coding versus intergenic sequence classification
- species classification
- enhancer prediction
- promoter prediction
- regulatory element prediction
- open chromatin region prediction

### 4. Nucleotide Transformer downstream tasks

The `nucleotide_transformer_downstream_tasks` group contains downstream genomic prediction tasks commonly used for evaluating DNA language models and genomic foundation models.

```text
raw_data/
└── nucleotide_transformer_downstream_tasks/
    ├── H3/
    ├── H3K14ac/
    ├── H3K36me3/
    ├── H3K4me1/
    ├── H3K4me2/
    ├── H3K4me3/
    ├── H3K79me3/
    ├── H3K9ac/
    ├── H4/
    ├── H4ac/
    ├── enhancers/
    ├── enhancers_types/
    ├── promoter_all/
    ├── promoter_no_tata/
    ├── promoter_tata/
    ├── splice_sites_acceptors/
    ├── splice_sites_all/
    └── splice_sites_donors/
```

Each task is stored as:

```text
<task_name>/
└── raw/
    ├── <task_name>.csv
    └── statistic.txt
```

These datasets support tasks such as:

- histone mark prediction
- enhancer prediction
- enhancer type classification
- promoter prediction
- TATA and non-TATA promoter classification
- splice site prediction
- chromatin-related sequence classification

## File Format

### CSV files

Most downstream benchmark datasets are provided as CSV files. A typical CSV file contains a DNA sequence column and one or more task-specific label or metadata columns.

Typical columns may include:

```text
sequence
label
split
```

or equivalent dataset-specific names such as:

```text
seq
fasta_seq
target
class
```

Please inspect the corresponding `statistic.txt` file for the exact schema of each dataset.

### Parquet files

Large-scale genome tiling and pretraining datasets are provided in Parquet format for more efficient storage and loading.

Typical columns may include:

```text
index
sequence / seq / fasta_seq
split
chromosome
start
end
species
additional metadata
```

The exact schema may vary by dataset.

### `statistic.txt`

Each VisualDNA-compatible raw dataset includes a `statistic.txt` file. This file provides dataset-level information and should be checked before training or evaluation.

It may include:

```text
number of samples
column names
sequence length statistics
label distribution
split distribution
metadata summary
```

## Usage

### Download the repository

```python
from huggingface_hub import snapshot_download

dataset_dir = snapshot_download(
    repo_id="hxxiang/dna_benchmarks",
    repo_type="dataset",
    local_dir="./dna_benchmarks",
)
```

### Load a CSV dataset

```python
import pandas as pd

path = "./dna_benchmarks/raw_data/genomic_benchmarks/human_nontata_promoters/raw/human_nontata_promoters.csv"
df = pd.read_csv(path)

print(df.head())
print(df.columns)
```

### Load a Parquet dataset

```python
import pandas as pd

path = "./dna_benchmarks/raw_data/hg38-2048/raw/hg38-2048.parquet"
df = pd.read_parquet(path)

print(df.head())
print(df.columns)
```

### Inspect dataset statistics

```python
with open(
    "./dna_benchmarks/raw_data/hg38-2048/raw/statistic.txt",
    "r",
    encoding="utf-8",
) as f:
    print(f.read())
```

### Inspect eQTL datasets after extracting the archive

```python
import zipfile
from pathlib import Path

zip_path = Path("./dna_benchmarks/raw_data/data_long_range_dna/eqtl.zip")
extract_dir = Path("./dna_benchmarks/raw_data/data_long_range_dna/eqtl")

with zipfile.ZipFile(zip_path, "r") as zf:
    zf.extractall(extract_dir)

for csv_path in sorted(extract_dir.glob("eqtl/*/raw/*.csv")):
    print(csv_path)
```

## Usage with VisualDNA

The `raw_data` directory is organized to be compatible with VisualDNA-style data processing. Each dataset is expected to follow:

```text
<dataset_name>/
└── raw/
    ├── <dataset_name>.csv or <dataset_name>.parquet
    └── statistic.txt
```

This format allows VisualDNA-based tools to locate and process genomic datasets in a consistent way.

VisualDNA can further convert these raw genomic sequences into model-specific representations, such as rendered visual genomic documents, pixel-based DNA images, or other supported formats.

## Usage with OpticalDNA

OpticalDNA uses selected datasets from this repository for genomic visual representation learning and benchmark evaluation.

In this setting, datasets in `raw_data` can be used as input to the OpticalDNA / VisualDNA pipeline. The pipeline may convert DNA sequences into visual representations and then use them for pretraining, fine-tuning, or evaluation.

This repository is not limited to OpticalDNA. The same datasets can also be used by conventional DNA language models, CNN-based sequence models, Transformer-based genomic models, and other genomic representation learning methods.

## Intended Use

This dataset collection is intended for research on:

- DNA foundation models
- genomic sequence modeling
- genomic benchmark evaluation
- regulatory genomics
- promoter and enhancer prediction
- chromatin profile prediction
- splice site prediction
- eQTL and long-range regulatory prediction
- genome-scale pretraining
- cross-species genomic representation learning
- visual and multimodal genomic representation learning

## Out-of-Scope Use

This dataset collection is not intended for:

- clinical diagnosis
- medical decision-making
- identifying individuals from genomic data
- inferring sensitive personal information
- direct therapeutic recommendation
- unvalidated biological or clinical interpretation

Any biological conclusions drawn from models trained on these datasets should be validated through appropriate downstream analyses and experimental evidence.

## Data Sources and Provenance

This repository aggregates and reformats datasets from multiple public genomic benchmark sources, including:

- Genomic Benchmarks datasets
- Long-range DNA / eQTL-related datasets
- Nucleotide Transformer downstream benchmark tasks
- Genome-scale human and rice sequence resources

The `official_data` directory preserves selected original downloaded files when available. The `raw_data` directory provides converted or VisualDNA-compatible versions.

Users should cite the original dataset sources when using specific subsets.

## License

This repository is released under the **MIT License**.

Please note that some files in `official_data` may originate from external public datasets. Users are responsible for following the licenses, terms of use, and citation requirements of the original data sources.

## Dataset Maintenance

This dataset collection is expected to grow over time. Future releases may include additional genomic benchmarks, larger pretraining corpora, more species, and additional multimodal genomic annotations.

The current repository layout is designed to support future extensions:

```text
official_data/
└── <source_dataset_collection>/

raw_data/
└── <benchmark_group>/
    └── <dataset_name>/
        └── raw/
            ├── <dataset_name>.csv or <dataset_name>.parquet
            └── statistic.txt
```

## Limitations

The datasets in this repository are collected from multiple sources and may differ in sequence length, label definition, split strategy, preprocessing procedure, species, and task formulation.

The `raw_data` files provide a unified storage layout, but they do not imply that all datasets share the same biological task, label space, or evaluation protocol.

Users should inspect each dataset and its corresponding `statistic.txt` file before conducting experiments.

For fair benchmarking, users should avoid mixing train, validation, and test splits unless the specific task protocol explicitly allows it.

## Citation

If you use this dataset collection, please cite this repository and the corresponding original data sources.

If you use this dataset with OpticalDNA, please also cite:

```bibtex
@inproceedings{xiang2026rethinking,
  title     = {Rethinking Genomic Modeling Through Optical Character Recognition},
  author    = {Xiang, Hongxin and Ma, Pengsen and Cao, Yunkang and Yu, Di and Chen, Haowen and Yang, Xinyu and Zeng, Xiangxiang},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  year      = {2026},
  url       = {https://openreview.net/forum?id=nggzekChuU}
}
```

For the arXiv version, please cite:

```bibtex
@article{xiang2026rethinking_arxiv,
  title   = {Rethinking Genomic Modeling Through Optical Character Recognition},
  author  = {Xiang, Hongxin and Ma, Pengsen and Cao, Yunkang and Yu, Di and Chen, Haowen and Yang, Xinyu and Zeng, Xiangxiang},
  journal = {arXiv preprint arXiv:2602.02014},
  year    = {2026},
  url     = {https://arxiv.org/abs/2602.02014}
}
```

Please also cite the original papers or repositories associated with the specific benchmark subsets you use.

## Contact

For questions or issues, please contact:

```text
xianghx@hnu.edu.cn
```