Datasets:
File size: 5,113 Bytes
a94d6fc 66eea04 a94d6fc 7011c52 a94d6fc 5d0164a a94d6fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
---
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.*
|