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license: mit
language:
- en
dataset_info:
- name: Orthoformer
description: Large-scale datasets for training and evaluating the Orthoformer foundation model for orthology and protein family representation learning.
config_name: default
splits:
- name: foundation_model_dataset
num_examples: 3000000
- name: Downstream_Tasks_dataset
- name: Orthoformer_eval_dataset
tags:
- protein
- orthology
- foundation-model
- bioinformatics
- embeddings
---
# Orthoformer Datasets
## 📌 Overview
**Orthoformer** is a large-scale genomics dataset designed to support **function-centric foundation modeling** of microbial and viral genomes.
Unlike conventional sequence-based models that infer biological roles from nucleotide or protein context, Orthoformer represents each genome by its **orthologous group composition and abundance**, treating **functional units rather than sequences** as the basic biological vocabulary.
The dataset was constructed from approximately **three million prokaryotic and viral genomes**, each encoded as a high-dimensional functional profile capturing:
- biochemical identity
- gene family dynamics
- evolutionary conservation
- pathway-scale metabolic capacity
These representations enable learning a **functional embedding space** that provides an **alignment-free measure of genomic similarity**, supporting robust taxonomy, phylogenetic analysis, and detection of functional convergence across microbial lineages.
The same functional embeddings generalize beyond evolutionary structure to predict:
- biosynthetic gene cluster abundance
- ecological niche differentiation
- organism-level phenotypes
- marker-gene associations
Together, the Orthoformer dataset establishes a **function-first framework for microbial genomics**, offering a scalable alternative to sequence-centric datasets for studying microbial evolution, function, and ecology.
---
## 📂 Dataset Structure
The repository is organized into three functional splits:
| Split | Purpose | Description |
|------|--------|-------------|
| `foundation_model_dataset` | Pretraining | Large-scale collection of microbial and viral genomes represented by orthologous group composition and abundance, used for self-supervised function-centric foundation model training |
| `Downstream_Tasks_dataset` | Fine-tuning | Labeled genome-level datasets for functional, metabolic, and ecological prediction tasks, including niche, biosynthetic capacity, and phenotype inference |
| `Orthoformer_eval_dataset` | Evaluation | Benchmark datasets for evaluating functional embeddings on taxonomy, phylogeny, functional convergence, and biological consistency |
---
## 📊 Dataset Statistics
| Split | Size | Max Sequence Length |
|------|------|--------------------|
| foundation_model_dataset | ~3M sequences | 2048 |
| Downstream_Tasks_dataset | Task dependent | Task dependent |
| Orthoformer_eval_dataset | Benchmarks | Task dependent |
---
## 🚀 Accessing the Dataset
You can download the dataset via Hugging Face using **Git + Xet (recommended for large files)**:
```bash
# Install git-xet (Linux)
curl -sSf https://raw.githubusercontent.com/huggingface/xet-core/main/git_xet/install.sh | sh
git xet install
# Clone the dataset
git clone https://huggingface.co/datasets/jackkuo/Orthoformer
````
If you only want the metadata without large files:
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/jackkuo/Orthoformer
```
---
## 🧬 Intended Use
The Orthoformer dataset is intended for training and evaluating **foundation models for microbial functional genomics**, including:
- Functional embedding of microbial and viral genomes
- Alignment-free phylogeny and taxonomy
- Functional convergence and evolutionary analysis
- Metabolic and pathway-level phenotype prediction
- Biosynthetic gene cluster and ecological niche inference
It is specifically designed for **function-centric modeling**, where orthologous gene groups are treated as the fundamental representation units instead of raw nucleotide or amino acid sequences.
---
## 📜 License
This dataset is released under the **MIT License**.
---
## 📖 Citation
If you use this dataset, please cite:
```bibtex
@dataset{xxx,
title = {Orthoformer: xxx},
author = {xxx},
year = {2025},
}
```
---
## 🔗 Related Resources
* **Model**: [https://huggingface.co/jackkuo/Orthoformer](https://huggingface.co/jackkuo/Orthoformer)
* **Code**: [https://github.com/JackKuo666/Orthoformer](https://github.com/JackKuo666/Orthoformer)
---
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