README.md

---
license: mit
task_categories:
- text-generation
tags:
- biology
- genomics
- long-context
size_categories:
- 100B<n<1T
---

# GENERator-v2-Eukaryote Gene-Centric Pretraining Corpus

This repository provides the **gene-centric pretraining corpus underlying GENERator-v2-Eukaryote**, a large-scale DNA language model for **eukaryotic genome understanding**.

The dataset is constructed by leveraging **RefSeq annotations** to extract biologically meaningful **functional genomic regions**, which serve as the foundation for large-context DNA language model pretraining.

---

## 📌 Dataset Construction Overview

The core design philosophy of this dataset is **gene-centric functional sequence modeling**.

High-confidence reference annotations (e.g. RefSeq) are used **as a scaffold** to identify and extract contiguous functional regions from eukaryotic genomes, including protein-coding genes and diverse RNA genes.

---

## 🧬 Data Schema

Each row in the dataset corresponds to one functional genomic segment.

| Column | Type | Description |
|------|------|-------------|
| `record_id` | string | RefSeq record identifier |
| `taxonomy` | string | Full taxonomic lineage (semicolon-separated) |
| `species_type` | string | High-level species category token |
| `gene_type` | string | Functional gene category token |
| `strand` | string | DNA strand in the reference genome (`<+>` or `<->`) |
| `sequence` | string | Extracted functional DNA sequence |
| `start` | int | Start coordinate of the functional region on the RefSeq record |
| `end` | int | End coordinate of the functional region on the RefSeq record |

---

## 🌍 Species Type Tokens (`species_type`)

Each sample is annotated with a coarse-grained evolutionary category:

| Token | Meaning |
|------|--------|
| `<prt>` | Protozoa |
| `<fng>` | Fungi |
| `<pln>` | Plant |
| `<inv>` | Invertebrate |
| `<vrt>` | Vertebrate (non-mammalian) |
| `<mam>` | Vertebrate (mammalian) |

---

## 🧠 Gene Type Tokens (`gene_type`)

Functional regions are categorized as follows:

| Token | Description |
|------|-------------|
| `<cds>` | Protein-coding gene (gene-centric region, not limited to CDS only) |
| `<pseudo>` | Pseudogene |
| `<tRNA>` | Transfer RNA gene |
| `<rRNA>` | Ribosomal RNA gene |
| `<ncRNA>` | Non-coding RNA |
| `<misc_RNA>` | RNA genes not assigned to a specific class |

---

## 🔁 Strand Orientation

- `<+>` denotes the positive strand
- `<->` denotes the negative strand in the reference genome

---

## 🔬 Sequence Characteristics

- Raw DNA sequences (`A/C/G/T/N`)
- Uppercase encoding
- `N` denotes ambiguous nucleotides
- No tokenization, masking, or augmentation is applied at this stage

This representation preserves **maximum flexibility** for downstream preprocessing and modeling strategies.

---

## 🚀 Intended Use

This dataset is designed to support:

- Large-scale **DNA language model pretraining**
- Gene-centric functional sequence modeling
- Cross-species and cross-gene-type representation learning
- Research in comparative and functional genomics

---

## 🧪 Relationship to GENERator-v2-Eukaryote Training

This repository provides **raw functional sequence data**.

The actual pretraining pipeline of **GENERator-v2-Eukaryote** applies additional post-processing steps, including:

- Sequence concatenation and segmentation
- Tokenization and phase augmentation

These steps are **not applied in this dataset** and are described in detail in the **GENERator-v2 Technical Report** (Comming Soon).

---

## 🔮 Future Data Releases

The training corpus for **GENERator-v2-Prokaryote** is currently under active evaluation and optimization.  
We plan to release the corresponding prokaryotic pretraining data **after thorough validation of data quality and downstream performance**.

In addition, the **GENERanno series of genome annotation datasets**, covering both **eukaryotic and prokaryotic genomes** at substantially larger scale, will be made publicly available in future releases.

Please stay tuned for updates.

---

## 🔗 Related Resources

For more information about the GENERator family of models and ongoing developments, please visit our GitHub repository:

👉 https://github.com/GenerTeam/

---

## 📝 Citation

```bibtex
@misc{wu2025generator,
    title={GENERator: A Long-Context Generative Genomic Foundation Model},
    author={Wei Wu and Qiuyi Li and Mingyang Li and Kun Fu and Fuli Feng and Jieping Ye and Hui Xiong and Zheng Wang},
    year={2025},
    eprint={2502.07272},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2502.07272},
}
```