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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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+
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+ # Dataset Card for LucaVirus-OpenVirus-Gene
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+
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+ ## 1. Dataset Summary
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+
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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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+
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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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+
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+ ## 2. Dataset Statistics
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+
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+ The dataset focuses solely on nucleotide sequences (genomes, genes, and fragments):
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+
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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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+
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+ ## 3. Data Structure & Format
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+
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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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+
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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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+
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+ Each directory (`train`, `dev`, `test`) contains one or more **CSV files** with headers.
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+
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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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+
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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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+
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+
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+ ## 4. Intended Use
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+
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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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+
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+ ## 5. Usage Example
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+
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+ You can extract the archive and load the genomic data using the following Python snippet:
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ ## 6. Related Resources
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+
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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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+ -
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+ ## 7. Citation
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+
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+ If you use this dataset in your research, please cite:
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+
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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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+
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+ ## 8. License
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+
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+ This dataset is released under the **MIT License**.
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+
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+ ## 9. Contact
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+
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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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+