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