| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - Biology |
| - Bioinformatics |
| - Virus |
| - Genomics |
| - Proteomics |
| - Nucleotide |
| - Protein |
| - Foundation Model |
| - LucaVirus |
| - LucaVirus-Gene |
| - AI4Bio |
| - AI4Science |
| - Nucleotide-Protein |
| task_categories: |
| - feature-extraction |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
| # Dataset Card for LucaVirus-OpenVirus-Gene |
|
|
| ## 1. Dataset Summary |
|
|
| **LucaVirus-OpenVirus-Gene** is a large-scale genomic dataset consisting exclusively of viral nucleotide sequences. It is a specialized subset of the **OpenVirus** corpus, curated specifically for the pre-training of the **LucaVirus-Gene** foundation model. |
|
|
| By focusing purely on viral genomes, this dataset provides a high-density corpus of **10.4 million** sequences, enabling models to capture the intricate evolutionary patterns, regulatory motifs, and genomic architectures of DNA and RNA viruses. |
|
|
| ## 2. Dataset Statistics |
|
|
| The dataset focuses solely on nucleotide sequences (genomes, genes, and fragments): |
|
|
| | Feature | Count / Description | |
| | :--- | :--- | |
| | **Total Sequences** | 10.4 Million | |
| | **Sequence Type** | Nucleotide (DNA/RNA) | |
| | **`obj_type` Identifier** | `gene` (Exclusive) | |
| | **Primary Use** | Pre-training for LucaVirus-Gene | |
| |
| ## 3. Data Structure & Format |
| |
| ### 3.1 File Organization |
| The dataset is provided as a compressed **`.tar`** archive. Upon extraction, the data is partitioned into three standard machine-learning subsets: |
| |
| ```text |
| LucaVirus-OpenVirus-Gene/dataset/v1.0/ |
| ├── train/ # Training set (primary corpus for genomic pre-training) |
| ├── dev/ # Validation set (for model selection and tuning) |
| └── test/ # Test set (for final evaluation and benchmarking) |
| ``` |
| |
| Each directory (`train`, `dev`, `test`) contains one or more **CSV files** with headers. |
| |
| ### 3.2 CSV Schema |
| All CSV files follow a consistent four-column schema: |
| |
| | Column Name | Description | Details | |
| | :--- | :--- |:-------------------------------------------------------------------------------------------------------| |
| | **`obj_id`** | Sample ID | Unique identifier for each viral sequence. | |
| | **`obj_type`** | Sequence Type | Set to `gene` for all entries in this dataset (Nucleotide). | |
| | **`obj_seq`** | Sequence Content | Raw nucleotide string (A, T(U), C, G, N). | |
| | **`obj_label`** | Label | Metadata, taxonomic info, or functional labels associated with the genome (Annotation, Bio Knowledge). | |
| |
| |
| ## 4. Intended Use |
| |
| - **Genomic Foundation Modeling**: Building models like **LucaVirus-Gene** that specialize in the "language of genomes." |
| - **Viral Evolution Studies**: Analyzing conserved nucleotide patterns across divergent viral lineages. |
| - **Regulatory Element Discovery**: Identifying viral gene boundaries, promoters, and other non-coding functional motifs. |
| |
| ## 5. Usage Example |
| |
| You can extract the archive and load the genomic data using the following Python snippet: |
| |
| ```python |
| import tarfile |
| import pandas as pd |
| import os |
| |
| # 1. Extract the genomic dataset |
| with tarfile.open("LucaVirus-OpenVirus-Gene.tar.gz", "r:gz") as tar: |
| tar.extractall(path="./LucaVirus-OpenVirus-Gene") |
| |
| with tarfile.open("LucaVirus-OpenVirus-Gene/dataset.tar.gz", "r:gz") as tar: |
| tar.extractall(path="./LucaVirus-OpenVirus-Gene/dataset") |
| |
| # 2. Load a sample from the training set |
| train_path = "./LucaVirus-OpenVirus-Gene/dataset/v1.0/train" |
| csv_files = [f for f in os.listdir(train_path) if f.endswith('.csv')] |
| |
| if csv_files: |
| # Load the first CSV file |
| df = pd.read_csv(os.path.join(train_path, csv_files[0])) |
| |
| # Verify the sequence type |
| print(f"Loaded {len(df)} genomic sequences.") |
| print(df[['obj_id', 'obj_seq']].head()) |
| ``` |
| |
| ## 6. Related Resources |
| |
| This dataset is a core component of the **LucaGroup** biological modeling ecosystem. |
| - **Full Corpus (Gene + Prot)**: [LucaVirus-OpenVirus-Gene-Prot](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Gene-Prot) |
| - **Protein Subset**: [LucaVirus-OpenVirus-Prot](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Prot) |
| - **Models**: Visit the [LucaVirus Collection](https://huggingface.co/collections/LucaGroup/lucavirus). |
| - |
| ## 7. Citation |
|
|
| If you use this dataset in your research, please cite: |
|
|
| ```bibtex |
| @article{lucavirus2025, |
| title={Predicting the Evolutionary and Functional Landscapes of Viruses with a Unified Nucleotide-Protein Language Model: LucaVirus.}, |
| author={Pan, Yuan-Fei* and He, Yong*. et al.}, |
| journal={bioRxiv}, |
| year={2025}, |
| url={https://www.biorxiv.org/content/early/2025/06/20/2025.06.14.659722} |
| } |
| ``` |
|
|
| ## 8. License |
|
|
| This dataset is released under the **MIT License**. |
|
|
| ## 9. Contact |
|
|
| *For further information, please visit the [LucaGroup GitHub](https://github.com/LucaOne), email to: [YongHe: sanyuan.hy@alibaba-inc.com, heyongcsat@gmail.com], or contact the team via the Hugging Face organization profile.* |
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