--- 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/ └── raw/ ├── .csv └── statistic.txt ``` or, for larger datasets: ```text raw_data/ └── / └── raw/ ├── .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 / └── raw/ ├── .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 / └── raw/ ├── .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 / └── raw/ ├── .csv or .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/ └── / raw_data/ └── / └── / └── raw/ ├── .csv or .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 ```