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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-Prot |
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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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- 1M<n<10M |
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--- |
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# Dataset Card for LucaVirus-OpenVirus-Prot |
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## 1. Dataset Summary |
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**LucaVirus-OpenVirus-Prot** is a large-scale proteomics dataset consisting exclusively of viral protein sequences. It is a specialized subset of the **OpenVirus** corpus, specifically curated for the pre-training of the **LucaVirus-Prot** (or LucaVirus-Protein) foundation model. |
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This dataset provides a comprehensive representation of the viral proteosphere, comprising **5.2 million** protein sequences. It is designed to enable biological models to learn the "language of proteins," capturing structural motifs, functional domains, and evolutionary signatures across a vast array of viral families. |
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## 2. Dataset Statistics |
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The dataset focuses strictly on amino acid sequences: |
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| Feature | Count / Description | |
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| :--- | :--- | |
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| **Total Sequences** | 5.2 Million | |
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| **Sequence Type** | Protein (Amino Acids) | |
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| **`obj_type` Identifier** | `prot` (Exclusive) | |
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| **Primary Use** | Pre-training for LucaVirus-Prot | |
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## 3. Data Structure & Format |
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### 3.1 File Organization |
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The dataset is distributed 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-Prot/dataset/v1.0/ |
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├── train/ # Training set (primary corpus for protein 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 protein sequence. | |
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| **`obj_type`** | Sequence Type | Set to `prot` for all entries in this dataset. | |
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| **`obj_seq`** | Sequence Content | Raw amino acid string (standard IUPAC codes). | |
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| **`obj_label`** | Annotations | Metadata, taxonomic info, or functional labels associated with the protein (Annotation, Bio Knowledge) | |
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## 4. Intended Use |
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- **Protein Foundation Modeling**: Building models like **LucaVirus-Prot** that specialize in understanding protein sequences and their biophysical properties. |
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- **Functional Annotation**: Developing tools to predict viral protein functions, domains, and active sites. |
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- **Virus-Host Interaction**: Studying how viral proteins interact with host cellular machinery based on sequence patterns. |
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## 5. Usage Example |
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You can extract the archive and load the protein 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 protein dataset |
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with tarfile.open("LucaVirus-OpenVirus-Prot.tar.gz", "r:gz") as tar: |
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tar.extractall(path="./LucaVirus-OpenVirus-Prot") |
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with tarfile.open("LucaVirus-OpenVirus-Prot/dataset.tar.gz", "r:gz") as tar: |
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tar.extractall(path="./LucaVirus-OpenVirus-Prot/dataset") |
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# 2. Load a sample from the training set |
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train_path = "./LucaVirus-OpenVirus-Prot/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)} protein sequences.") |
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print(df[['obj_id', 'obj_seq', 'obj_label']].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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- **Genomic Subset**: [LucaVirus-OpenVirus-Gene](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Gene) |
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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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