update

README.md

@@ -2,26 +2,62 @@
 license: apache-2.0
 ---
 
-# 🧬 OmniGenBench Hub: Curated Genomic Datasets for AI Research
+# 🧬 OmniGenBench Hub: Unified Repository for Genomic Foundation Model Resources
 
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-[![Datasets](https://img.shields.io/badge/Datasets-3-green.svg)](#available-datasets)
+[![Benchmarks](https://img.shields.io/badge/Benchmarks-5-blue.svg)](#benchmarks)
+[![Datasets](https://img.shields.io/badge/Datasets-5-green.svg)](#datasets)
 [![Framework](https://img.shields.io/badge/Framework-OmniGenBench-orange.svg)](https://github.com/yangheng95/OmniGenBench)
 
-Welcome to **OmniGenBench Hub** - your one-stop repository for high-quality, curated genomic datasets designed specifically for AI and machine learning research in computational biology. Our hub provides seamlessly integrated datasets that work directly with the OmniGenBench framework, enabling researchers to focus on model development rather than data preprocessing.
+Welcome to **OmniGenBench Hub** - the unified, centralized repository for genomic foundation model resources. This hub consolidates all benchmarks, datasets, models, and pipelines required by the OmniGenBench framework, providing researchers with a single source of truth for reproducible genomic AI research.
+
+## 📦 Repository Structure
+
+```
+OmniGenBench_Hub/
+├── benchmarks/              # Benchmark suites (RGB, GUE, BEACON, etc.)
+│   ├── benchmarks_info.json
+│   ├── RGB.zip
+│   ├── GUE.zip
+│   ├── BEACON.zip
+│   ├── GB.zip
+│   ├── PGB.zip
+│   └── ...
+├── datasets/                # Individual datasets
+│   ├── datasets_info.json
+│   ├── deepsea_tfb_prediction.zip
+│   ├── translation_efficiency_prediction.zip
+│   └── variant_effect_prediction.zip
+├── models/                  # Pre-trained models (future)
+│   └── models_info.json
+└── pipelines/              # Ready-to-use pipelines (future)
+    └── pipelines_info.json
+```
 
 ## 🎯 What is OmniGenBench Hub?
 
-OmniGenBench Hub is a centralized collection of genomic datasets that have been:
-- ✅ **Carefully curated** and quality-controlled for research use
-- ✅ **Standardized** for consistent formatting and structure
-- ✅ **Optimized** for seamless integration with OmniGenBench framework
-- ✅ **Validated** through extensive testing and benchmarking
-- ✅ **Documented** with comprehensive metadata and usage examples
+OmniGenBench Hub serves as the **unified data infrastructure** for the OmniGenBench framework, providing:
+
+- ✅ **Centralized Storage**: All resources in one place instead of scattered repositories
+- ✅ **Organized Structure**: Clear subdirectories (benchmarks/, datasets/, models/, pipelines/)
+- ✅ **Metadata Rich**: Comprehensive JSON metadata files for programmatic access
+- ✅ **Backward Compatible**: Seamless migration from legacy Space repositories
+- ✅ **Standardized Formats**: Consistent data formats and structures across all resources
+- ✅ **Validated Content**: All resources tested through the OmniGenBench framework
 
 ## 🚀 Quick Start
 
-Getting started with our datasets is incredibly simple:
+### Download Benchmarks
+
+```bash
+# Using OmniGenBench CLI (automatically uses this hub)
+ogb autobench -m yangheng/PlantRNA-FM -b RGB
+
+# The framework will automatically download from:
+# https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/benchmarks/RGB.zip
+```
+
+### Download Datasets
 
 ```python
 from omnigenbench import OmniDatasetForSequenceClassification, OmniTokenizer
@@ -29,353 +65,319 @@ from omnigenbench import OmniDatasetForSequenceClassification, OmniTokenizer
 # Initialize tokenizer
 tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
 
-# Load any dataset from the hub
+# Load dataset from hub (automatically downloads from this repository)
 datasets = OmniDatasetForSequenceClassification.from_hub(
     dataset_name="deepsea_tfb_prediction",
     tokenizer=tokenizer,
     max_length=512
 )
 
-# That's it! Your datasets are ready for training and evaluation
-# Access train, validation, and test splits
+# Access splits
 train_dataset = datasets['train']
 valid_dataset = datasets['valid']
 test_dataset = datasets['test']
 ```
 
-## 📊 Available Datasets
+### Direct Download via HuggingFace Hub API
 
-Our hub currently hosts **3 high-quality datasets** spanning multiple genomic AI tasks. Each dataset is carefully organized with data files and configuration scripts.
+```python
+from huggingface_hub import snapshot_download
 
-### 📋 Dataset Categories and Overview
+# Download entire benchmark
+benchmark_path = snapshot_download(
+    repo_id="yangheng/OmniGenBench_Hub",
+    repo_type="dataset",
+    allow_patterns="benchmarks/RGB.zip"
+)
 
-| Category | Dataset Name | Task Type | Description | Size |
-|----------|-------------|-----------|-------------|------|
-| **🧬 Gene Regulation** | [`deepsea_tfb_prediction`](https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/deepsea_tfb_prediction.zip) | Classification | Transcription Factor Binding Site Prediction | 822 MB |
-| **🔬 Protein Synthesis** | [`translation_efficiency_prediction`](https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/translation_efficiency_prediction.zip) | Regression | mRNA Translation Efficiency Prediction | 868 KB |
-| **⚗️ Functional Genomics** | [`variant_effective_prediction`](https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/variant_effective_prediction.zip) | Multi-class | Variant Effect Prediction on Protein Function | 1.64 GB |
+# Download specific dataset
+dataset_path = snapshot_download(
+    repo_id="yangheng/OmniGenBench_Hub",
+    repo_type="dataset",
+    allow_patterns="datasets/translation_efficiency_prediction.zip"
+)
+```
 
-### 📖 Detailed Dataset Descriptions
+## 📊 Available Resources
 
-#### 1. 🧬 DeepSEA TFB Prediction (`deepsea_tfb_prediction`)
-- **Research Area**: Gene Regulation and Transcription Factor Binding
-- **Task**: Binary/Multi-class classification of transcription factor binding sites
-- **Applications**: 
-  - Regulatory element discovery
-  - Gene expression prediction
-  - Epigenomic analysis
-  - Drug target identification
-- **Data Format**: DNA sequences with binding labels
-- **Usage Example**:
-```python
-from omnigenbench import OmniDatasetForSequenceClassification, OmniTokenizer
+### 🧪 Benchmarks
 
-tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
-tfb_datasets = OmniDatasetForSequenceClassification.from_hub(
-    dataset_name="deepsea_tfb_prediction",
-    tokenizer=tokenizer,
-    max_length=512
-)
-# Access the training dataset
-train_data = tfb_datasets['train']
-```
+Our hub hosts **5 comprehensive benchmark suites** covering RNA and DNA analysis tasks:
 
-#### 2. 🔬 Translation Efficiency Prediction (`translation_efficiency_prediction`)
-- **Research Area**: Protein Synthesis and mRNA Biology
-- **Task**: Regression for predicting ribosome loading efficiency
-- **Applications**:
-  - mRNA therapeutic design
-  - Protein production optimization
-  - Synthetic biology applications
-  - Gene expression engineering
-- **Data Format**: mRNA sequences with continuous efficiency scores
-- **Usage Example**:
-```python
-from omnigenbench import OmniDatasetForSequenceRegression, OmniTokenizer
+| Benchmark | Genome | Tasks | Task Types | Species | Description |
+|-----------|--------|-------|------------|---------|-------------|
+| **RGB** | RNA | 10 | Classification, Token Classification | Multi-species | RNA Genome Benchmark - Comprehensive RNA understanding |
+| **BEACON** | RNA | 13 | Classification, Regression | Multi-species | Benchmarking Environment for RNA Computational Methods |
+| **GUE** | DNA | 28 | Classification | Multi-species | Genomic Understanding Evaluation |
+| **GB** | DNA | 9 | Classification | Multi-species | Classic Genomic Benchmark |
+| **PGB** | DNA | 7 | Classification, Regression | Plant | Plant Genomics Benchmark |
 
-tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
-translation_datasets = OmniDatasetForSequenceRegression.from_hub(
-    dataset_name="translation_efficiency_prediction",
-    tokenizer=tokenizer,
-    max_length=256
-)
-# Access the training dataset
-train_data = translation_datasets['train']
-```
+**Download locations**: `benchmarks/<benchmark_name>.zip`  
+**Metadata**: `benchmarks/benchmarks_info.json`
 
-#### 3. ⚗️ Variant Effect Prediction (`variant_effective_prediction`)
-- **Research Area**: Functional Genomics and Precision Medicine
-- **Task**: Multi-class classification of genetic variant effects
-- **Applications**:
-  - Clinical variant interpretation
-  - Precision medicine
-  - Pharmacogenomics
-  - Disease risk assessment
-- **Data Format**: Protein sequences with variant effect annotations
-- **Usage Example**:
-```python
-from omnigenbench import OmniDatasetForTokenClassification, OmniTokenizer
+### 📁 Datasets
 
-tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
-variant_datasets = OmniDatasetForTokenClassification.from_hub(
-    dataset_name="variant_effective_prediction",
-    tokenizer=tokenizer,
-    max_length=1024
-)
-# Access the training dataset
-train_data = variant_datasets['train']
-```
+**5 curated individual datasets** for specific genomic tasks:
 
-## 📁 Dataset Structure
+| Dataset | Genome | Task Type | Species | Description |
+|---------|--------|-----------|---------|-------------|
+| **deepsea_tfb_prediction** | DNA | Classification | Human | Transcription Factor Binding prediction |
+| **translation_efficiency_prediction** | RNA | Regression | Multi-species | mRNA translation efficiency |
+| **variant_effect_prediction** | DNA | Classification | Human | Genomic variant effect prediction |
+| **RNA-SSP-Archive2** | RNA | Token Classification | Multi-species | RNA Secondary Structure Prediction |
+| **RNA-mRNA** | RNA | Classification | Multi-species | RNA mRNA classification |
 
-Each dataset in our hub follows a standardized structure for consistency and ease of use:
+**Download locations**: `datasets/<dataset_name>.zip`  
+**Metadata**: `datasets/datasets_info.json`
 
-```
-dataset_name.zip
-├── data/
-│   ├── train.csv          # Training data
-│   ├── valid.csv          # Validation data
-│   ├── test.csv           # Test data
-│   └── metadata.json      # Dataset metadata
-├── config.py              # Dataset configuration and loading scripts
-└── README.md              # Dataset-specific documentation
-```
+### 🤖 Models (Coming Soon)
 
-## 🛠️ Integration with OmniGenBench
+Pre-trained genomic foundation models will be hosted here in future releases. Currently, models are available directly on HuggingFace Hub:
+- [yangheng/OmniGenome-186M](https://huggingface.co/yangheng/OmniGenome-186M)
+- [yangheng/OmniGenome-52M](https://huggingface.co/yangheng/OmniGenome-52M)
+- [yangheng/PlantRNA-FM](https://huggingface.co/yangheng/PlantRNA-FM)
+- And 30+ more models...
 
-Our datasets are designed to work seamlessly with the OmniGenBench ecosystem:
+**Metadata**: `models/models_info.json`
 
-### For Classification Tasks
-```python
-from omnigenbench import OmniDatasetForSequenceClassification, OmniTokenizer
+### � Pipelines (Coming Soon)
 
-# Initialize tokenizer
-tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
+Ready-to-use analysis pipelines will be added in future releases.
 
-# Load classification datasets
-datasets = OmniDatasetForSequenceClassification.from_hub(
-    dataset_name="deepsea_tfb_prediction",
-    tokenizer=tokenizer,
-    max_length=512
-)
+**Metadata**: `pipelines/pipelines_info.json`
 
-# Access individual splits
-train_dataset = datasets['train']
-valid_dataset = datasets['valid']
-test_dataset = datasets['test']
-```
 
-### For Regression Tasks
-```python
-from omnigenbench import OmniDatasetForSequenceRegression, OmniTokenizer
+## 🔧 Technical Details
 
-# Initialize tokenizer
-tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
+### Metadata Files
 
-# Load regression datasets
-datasets = OmniDatasetForSequenceRegression.from_hub(
-    dataset_name="translation_efficiency_prediction", 
-    tokenizer=tokenizer,
-    max_length=256
-)
+Each resource category includes a metadata JSON file for programmatic access:
 
-# Access individual splits
-train_dataset = datasets['train']
-valid_dataset = datasets['valid']
-test_dataset = datasets['test']
+#### `benchmarks/benchmarks_info.json`
+```json
+{
+  "RGB": {
+    "filename": "RGB.zip",
+    "genome": "RNA",
+    "species": "multi-species",
+    "task_number": 10,
+    "task_type": "sequence_classification, token_classification",
+    "description": "RNA Genome Benchmark",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  }
+}
 ```
 
-### For Token-level Tasks
-```python
-from omnigenbench import OmniDatasetForTokenClassification, OmniTokenizer
+#### `datasets/datasets_info.json`
+```json
+{
+  "deepsea_tfb_prediction": {
+    "filename": "deepsea_tfb_prediction.zip",
+    "genome": "DNA",
+    "species": "human",
+    "task_type": "sequence_classification",
+    "description": "DeepSEA TFB prediction",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  }
+}
+```
 
-# Initialize tokenizer
-tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
+### Download URLs
 
-# Load token-level datasets
-datasets = OmniDatasetForTokenClassification.from_hub(
-    dataset_name="variant_effective_prediction",
-    tokenizer=tokenizer,
-    max_length=1024
-)
+All resources follow a consistent URL pattern:
 
-# Access individual splits
-train_dataset = datasets['train']
-valid_dataset = datasets['valid']
-test_dataset = datasets['test']
+```
+https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/<category>/<filename>
 ```
 
-## 🎓 Educational Resources
+Examples:
+- Benchmark: `https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/benchmarks/RGB.zip`
+- Dataset: `https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/datasets/deepsea_tfb_prediction.zip`
+- Metadata: `https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/benchmarks/benchmarks_info.json`
 
-Each dataset comes with comprehensive educational materials:
+### Automated Downloads
 
-- **📚 Tutorial Notebooks**: Step-by-step guides for using each dataset
-- **🔬 Scientific Background**: Detailed biological context and significance
-- **💡 Best Practices**: Recommended approaches and methodologies
-- **📊 Benchmark Results**: Performance baselines from state-of-the-art models
+The OmniGenBench framework automatically downloads resources from this hub with fallback support:
 
-## 🌟 Key Features
+1. **Primary**: New unified Hub structure (this repository)
+2. **Fallback**: Legacy Space repository (deprecated)
+3. **Cache**: Local cache if network unavailable
 
-### ✨ **Plug-and-Play Integration**
-- One-line dataset loading with automatic preprocessing
-- Compatible with all major deep learning frameworks
-- Seamless integration with Hugging Face ecosystem
+```python
+# Framework handles downloads automatically
+from omnigenbench import AutoBench
 
-### 🔧 **Standardized Format**
-- Consistent data structure across all datasets
-- Unified API for different task types
-- Automatic train/validation/test splitting
+# This will automatically download RGB.zip from this hub
+bench = AutoBench(benchmark="RGB", config_or_model="yangheng/OmniGenome-186M")
+bench.run()
+```
+
+## 📁 Standard Data Structure
 
-### 📈 **Research-Ready**
-- High-quality, peer-reviewed datasets
-- Comprehensive evaluation metrics
-- Reproducible benchmark results
+### Benchmark Structure
+```
+<benchmark_name>.zip
+├── <task_1>/
+│   ├── train.json
+│   ├── test.json
+│   ├── config.py
+│   └── metadata.py
+├── <task_2>/
+│   └── ...
+└── metadata.py
+```
 
-### 🚀 **Performance Optimized**
-- Efficient data loading and caching
-- Memory-optimized for large-scale training
-- Multi-processing support for faster iteration
+### Dataset Structure
+```
+<dataset_name>.zip
+├── data/
+│   ├── train.json
+│   ├── valid.json
+│   ├── test.json
+│   └── metadata.json
+├── config.py
+└── README.md
+```
+
+## 🛠️ Integration with OmniGenBench
+
+### Using Benchmarks
+
+```bash
+# Download and evaluate on RGB benchmark
+ogb autobench -m yangheng/PlantRNA-FM -b RGB
+
+# Download and evaluate on multiple benchmarks
+ogb autobench -m yangheng/OmniGenome-186M -b RGB,GUE,BEACON
+```
 
-## 📋 Usage Guidelines
+### Using Datasets
 
-### Basic Usage Pattern
 ```python
-# 1. Import the framework
-from omnigenbench import (
-    OmniDatasetForSequenceClassification,
-    OmniTokenizer,
-    OmniModelForSequenceClassification,
-    Trainer
-)
+from omnigenbench import OmniDatasetForSequenceClassification, OmniTokenizer
 
-# 2. Initialize tokenizer
+# Initialize tokenizer
 tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
 
-# 3. Load your chosen dataset
+# Load dataset (automatically downloads from this hub)
 datasets = OmniDatasetForSequenceClassification.from_hub(
     dataset_name="deepsea_tfb_prediction",
     tokenizer=tokenizer,
     max_length=512
 )
 
-# 4. Initialize model
-model = OmniModelForSequenceClassification(
-    model_name_or_path="yangheng/OmniGenome-52M",
-    tokenizer=tokenizer,
-    num_labels=2  # Adjust based on your task
-)
+# Access splits
+train_dataset = datasets['train']
+valid_dataset = datasets['valid']
+test_dataset = datasets['test']
+```
 
-# 5. Create trainer and start training
-trainer = Trainer(
-    model=model,
-    train_dataset=datasets['train'],
-    valid_dataset=datasets['valid'],
-    epochs=10,
-    batch_size=16,
-    learning_rate=1e-4
-)
+### Direct HuggingFace Hub API
 
-# 6. Train the model
-trainer.train()
+```python
+from huggingface_hub import hf_hub_download
 
-# 7. Evaluate on test set
-test_results = trainer.evaluate(datasets['test'])
-print(f"Test results: {test_results}")
+# Download specific file
+file_path = hf_hub_download(
+    repo_id="yangheng/OmniGenBench_Hub",
+    filename="benchmarks/RGB.zip",
+    repo_type="dataset"
+)
 
-# 8. Inference on new sequences
-predictions = model.inference(["ATCGATCGATCG", "GCTAGCTAGCTA"])
-print(f"Predictions: {predictions}")
+# Download metadata
+metadata_path = hf_hub_download(
+    repo_id="yangheng/OmniGenBench_Hub",
+    filename="benchmarks/benchmarks_info.json",
+    repo_type="dataset"
+)
 ```
 
-### Advanced Configuration
-```python
-from omnigenbench import OmniDatasetForSequenceRegression, OmniTokenizer
+## 🌟 Key Features
 
-# Initialize tokenizer
-tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
+- ✅ **Unified Repository**: All resources in one centralized location
+- ✅ **Organized Structure**: Clear subdirectories for benchmarks, datasets, models, pipelines
+- ✅ **Metadata Rich**: Comprehensive JSON metadata for programmatic access
+- ✅ **Auto-Download**: Framework automatically downloads from this hub
+- ✅ **Backward Compatible**: Fallback support for legacy repositories
+- ✅ **Standardized Formats**: Consistent data structures across all resources
+- ✅ **Research-Ready**: Validated and tested with OmniGenBench framework
 
-# Custom configuration for specific needs
-datasets = OmniDatasetForSequenceRegression.from_hub(
-    dataset_name="translation_efficiency_prediction",
-    tokenizer=tokenizer,
-    max_length=512,
-    cache_dir="./cache"
-)
+## � Documentation
 
-# Create DataLoader with custom settings
-train_loader = datasets['train'].get_dataloader(
-    batch_size=32,
-    shuffle=True,
-    num_workers=4,
-    pin_memory=True
-)
-
-# Use in training loop
-for batch in train_loader:
-    input_ids = batch['input_ids']
-    attention_mask = batch['attention_mask']
-    labels = batch['labels']
-    # Your training code here...
-```
+For comprehensive guides and tutorials, please visit:
+- **Framework Documentation**: [OmniGenBench Docs](https://github.com/yangheng95/OmniGenBench)
+- **Getting Started Guide**: [GETTING_STARTED.md](https://github.com/yangheng95/OmniGenBench/blob/main/docs/GETTING_STARTED.md)
+- **API Reference**: [API Documentation](https://github.com/yangheng95/OmniGenBench/tree/main/docs)
+- **Example Notebooks**: [examples/](https://github.com/yangheng95/OmniGenBench/tree/main/examples)
 
 ## 🤝 Contributing
 
-We welcome contributions to expand our dataset collection! If you have a high-quality genomic dataset that would benefit the research community:
+We welcome contributions to expand our resource collection! To contribute:
 
-1. **Format your dataset** according to our standards
-2. **Include comprehensive documentation** and metadata
-3. **Provide benchmark results** using standard evaluation metrics
-4. **Submit a pull request** with your dataset and documentation
+1. Format your resource according to our standards
+2. Include comprehensive metadata and documentation
+3. Test with OmniGenBench framework
+4. Submit a pull request to [OmniGenBench repository](https://github.com/yangheng95/OmniGenBench)
 
-## 📄 Licensing
+## 📄 License
 
-All datasets in OmniGenBench Hub are released under the **Apache 2.0 License**, ensuring:
-- ✅ Free use for research and commercial applications
-- ✅ Modification and redistribution rights
-- ✅ Patent protection for users
-- ✅ Clear attribution requirements
+All resources in OmniGenBench Hub are released under the **Apache 2.0 License**, ensuring:
+- Free use for research and commercial applications
+- Modification and redistribution rights
+- Patent protection for users
+- Clear attribution requirements
 
-## 📞 Support and Contact
+## 📞 Support
 
-- **Documentation**: [OmniGenBench Docs](https://omnigenbench.readthedocs.io/)
-- **Issues**: [GitHub Issues](https://github.com/yangheng95/OmniGenBench/issues)
-- **Discussions**: [GitHub Discussions](https://github.com/yangheng95/OmniGenBench/discussions)
-- **Email**: Contact the maintainer at [yangheng@exeter.ac.uk]
+- **GitHub Issues**: [Report bugs or request features](https://github.com/yangheng95/OmniGenBench/issues)
+- **GitHub Discussions**: [Ask questions and share ideas](https://github.com/yangheng95/OmniGenBench/discussions)
+- **Email**: yangheng@exeter.ac.uk
 
-## 🎯 Roadmap
+## � Citation
 
-### Coming Soon
-- 📊 **Additional Dataset Categories**: Epigenomics, Proteomics, Multi-omics
-- 🔧 **Enhanced Tools**: Advanced preprocessing utilities and evaluation metrics
-- 🌐 **Community Features**: User-contributed datasets and collaborative benchmarks
-- 📱 **API Expansion**: REST API for programmatic access
+If you use resources from OmniGenBench Hub in your research, please cite:
 
-### Future Releases
-- **10+ new datasets** covering diverse genomic tasks
-- **Automated benchmarking** pipeline for consistent evaluation
-- **Multi-modal datasets** combining sequence, structure, and functional data
-- **Real-time dataset updates** and version control
+```bibtex
+@software{omnigenbench2025,
+  author = {Yang, Heng},
+  title = {OmniGenBench: A Unified Framework for Genomic Foundation Models},
+  year = {2025},
+  url = {https://github.com/yangheng95/OmniGenBench}
+}
+```
+
+## 📊 Statistics
 
-## 🏆 Acknowledgments
+| Category | Count | Description |
+|----------|-------|-------------|
+| **Benchmarks** | 5 | Comprehensive evaluation suites (RGB, GUE, BEACON, GB, PGB) |
+| **Datasets** | 5 | Curated individual datasets for specific tasks |
+| **Models** | Coming Soon | Pre-trained genomic foundation models |
+| **Pipelines** | Coming Soon | Ready-to-use analysis pipelines |
+| **Total Tasks** | 67+ | Combined tasks across all benchmarks |
 
-OmniGenBench Hub is built on the foundation of outstanding research from the genomics and AI communities. We thank:
-- The original dataset creators and research teams
-- The open-source community for tools and frameworks
-- Beta testers and early adopters for valuable feedback
-- The Hugging Face team for hosting infrastructure
+## 🗺️ Roadmap
 
-## 📊 Dataset Statistics Summary
+### Current Release (v1.0)
+- ✅ 7 benchmark suites with 69+ tasks
+- ✅ 3 curated datasets
+- ✅ Organized folder structure
+- ✅ Comprehensive metadata files
+- ✅ Automated download integration
 
-| Metric | Total |
-|--------|--------|
-| **Total Datasets** | 3 |
-| **Total Size** | 2.46 GB |
-| **Task Types** | Classification, Regression, Multi-class |
-| **Sequence Types** | DNA, RNA, Protein |
-| **Research Areas** | Gene Regulation, Protein Synthesis, Functional Genomics |
+### Future Releases
+- 📦 Pre-trained model hosting
+- � Analysis pipeline templates
+- 📊 Additional benchmark suites
+- 🌐 More individual datasets
+- 🔄 Continuous updates and improvements
 
 ---
 
-**🧬 Start your genomic AI journey today with OmniGenBench Hub!**
+**🧬 OmniGenBench Hub - Unified Infrastructure for Genomic Foundation Model Research**
 
-*Empowering researchers worldwide with high-quality genomic datasets for breakthrough discoveries in computational biology.*
+*Maintained by YANG, HENG | [Homepage](https://yangheng95.github.io) | [GitHub](https://github.com/yangheng95)*

STRUCTURE.md

@@ -0,0 +1,170 @@
+# OmniGenBench Hub Directory Structure
+
+This document describes the organization of resources in the OmniGenBench Hub repository.
+
+## Directory Layout
+
+```
+OmniGenBench_Hub/
+├── benchmarks/              # Benchmark suites for model evaluation
+│   ├── benchmarks_info.json # Metadata for all benchmarks
+│   ├── RGB.zip              # RNA Genome Benchmark
+│   ├── BEACON.zip           # RNA Computational Methods Benchmark
+│   ├── GUE.zip              # Genomic Understanding Evaluation
+│   ├── GB.zip               # Classic Genomic Benchmark
+│   └── PGB.zip              # Plant Genomics Benchmark
+│
+├── datasets/                # Individual curated datasets
+│   ├── datasets_info.json   # Metadata for all datasets
+│   ├── deepsea_tfb_prediction.zip
+│   ├── translation_efficiency_prediction.zip
+│   ├── variant_effect_prediction.zip
+│   ├── RNA-SSP-Archive2.zip # RNA Secondary Structure Prediction
+│   └── RNA-mRNA.zip         # RNA mRNA Classification
+│
+├── models/                  # Pre-trained models (future)
+│   └── models_info.json     # Metadata for models
+│
+├── pipelines/               # Analysis pipelines (future)
+│   └── pipelines_info.json  # Metadata for pipelines
+│
+└── README.md                # Main documentation
+
+```
+
+## Resource Categories
+
+### Benchmarks (benchmarks/)
+Comprehensive evaluation suites containing multiple tasks:
+- **RGB**: 10 RNA understanding tasks
+- **BEACON**: 13 RNA computational tasks
+- **GUE**: 28 genomic understanding tasks
+- **GB**: 9 classic DNA classification tasks
+- **PGB**: 7 plant genomic tasks
+
+### Datasets (datasets/)
+Individual curated datasets for specific tasks:
+- **deepsea_tfb_prediction**: Transcription factor binding prediction (784 MB)
+- **translation_efficiency_prediction**: mRNA translation efficiency (848 KB)
+- **variant_effect_prediction**: Genomic variant effects (1.6 GB)
+- **RNA-SSP-Archive2**: RNA secondary structure prediction (49 KB)
+- **RNA-mRNA**: RNA mRNA classification (3.0 MB)
+
+### Models (models/)
+Pre-trained genomic foundation models (coming soon).
+Current models are hosted on HuggingFace Hub:
+- yangheng/OmniGenome-186M
+- yangheng/OmniGenome-52M
+- yangheng/PlantRNA-FM
+- And 30+ more...
+
+### Pipelines (pipelines/)
+Ready-to-use analysis pipelines (coming soon).
+
+## Download URLs
+
+All resources follow this URL pattern:
+```
+https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/<category>/<filename>
+```
+
+Examples:
+- Benchmark: `https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/benchmarks/RGB.zip`
+- Dataset: `https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/datasets/deepsea_tfb_prediction.zip`
+- Metadata: `https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/resolve/main/benchmarks/benchmarks_info.json`
+
+## Metadata Files
+
+Each category contains a `*_info.json` file with comprehensive metadata:
+
+### benchmarks_info.json
+```json
+{
+  "RGB": {
+    "filename": "RGB.zip",
+    "genome": "RNA",
+    "species": "multi-species",
+    "task_number": 10,
+    "task_type": "sequence_classification, token_classification",
+    "description": "RNA Genome Benchmark",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  }
+}
+```
+
+### datasets_info.json
+```json
+{
+  "deepsea_tfb_prediction": {
+    "filename": "deepsea_tfb_prediction.zip",
+    "genome": "DNA",
+    "species": "human",
+    "task_type": "sequence_classification",
+    "description": "DeepSEA TFB prediction",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  }
+}
+```
+
+## Usage with OmniGenBench
+
+The OmniGenBench framework automatically downloads from this organized structure:
+
+```bash
+# Benchmarks
+ogb autobench -m yangheng/PlantRNA-FM -b RGB
+
+# Downloads from: benchmarks/RGB.zip
+```
+
+```python
+# Datasets
+from omnigenbench import OmniDatasetForSequenceClassification, OmniTokenizer
+
+tokenizer = OmniTokenizer.from_pretrained("yangheng/OmniGenome-52M")
+datasets = OmniDatasetForSequenceClassification.from_hub(
+    dataset_name="deepsea_tfb_prediction",
+    tokenizer=tokenizer
+)
+# Downloads from: datasets/deepsea_tfb_prediction.zip
+```
+
+## File Sizes
+
+### Benchmarks
+- RGB.zip: 34 MB
+- BEACON.zip: 51 MB
+- GUE.zip: 80 MB
+- GB.zip: 85 MB
+- PGB.zip: 122 MB
+- **Total**: ~372 MB
+
+### Datasets
+- deepsea_tfb_prediction.zip: 784 MB
+- translation_efficiency_prediction.zip: 848 KB
+- variant_effect_prediction.zip: 1.6 GB
+- RNA-SSP-Archive2.zip: 49 KB
+- RNA-mRNA.zip: 3.0 MB
+- **Total**: ~2.4 GB
+
+### Grand Total
+~2.8 GB of curated genomic data for AI research
+
+## Migration Notes
+
+This repository replaces the legacy Space-based storage:
+- **Old**: `https://huggingface.co/spaces/yangheng/OmniGenomeLeaderboard/`
+- **New**: `https://huggingface.co/datasets/yangheng/OmniGenBench_Hub/`
+
+The OmniGenBench framework (v0.3.29+) automatically uses the new structure with fallback to legacy.
+
+## Maintenance
+
+**Last Updated**: November 8, 2025  
+**Maintainer**: YANG, HENG  
+**Contact**: yangheng@exeter.ac.uk  
+**License**: Apache 2.0

BEACON.zip → benchmarks/BEACON.zip

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+size 58926037

GB.zip → benchmarks/GB.zip

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GUE.zip → benchmarks/GUE.zip

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+size 75866865

PGB.zip → benchmarks/PGB.zip

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+oid sha256:53aff3fb85b5598045cd4e897999b253ab50f39f8074c55a2e3c7db3508948f7
+size 127678083

RGB.zip → benchmarks/RGB.zip

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-size 35101774
+oid sha256:a7cc52cf4660fc383dc6de89fe1a5365027a1206eb3d361248472d1b4a3a860b
+size 35388889

benchmarks/benchmarks_info.json

@@ -0,0 +1,57 @@
+{
+  "RGB": {
+    "filename": "RGB.zip",
+    "genome": "RNA",
+    "species": "multi-species",
+    "task_number": 10,
+    "task_type": "sequence_classification, token_classification",
+    "description": "RNA Genome Benchmark - Comprehensive evaluation suite for RNA sequence understanding",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "BEACON": {
+    "filename": "BEACON.zip",
+    "genome": "RNA",
+    "species": "multi-species",
+    "task_number": 13,
+    "task_type": "sequence_classification, regression",
+    "description": "Benchmarking Environment for RNA Computational Methods",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "GUE": {
+    "filename": "GUE.zip",
+    "genome": "DNA",
+    "species": "multi-species",
+    "task_number": 28,
+    "task_type": "sequence_classification",
+    "description": "Genomic Understanding Evaluation - DNA sequence classification benchmark",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "GB": {
+    "filename": "GB.zip",
+    "genome": "DNA",
+    "species": "multi-species",
+    "task_number": 9,
+    "task_type": "sequence_classification",
+    "description": "Genomic Benchmark - Classic DNA classification tasks",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "PGB": {
+    "filename": "PGB.zip",
+    "genome": "DNA",
+    "species": "plant",
+    "task_number": 7,
+    "task_type": "sequence_classification, regression",
+    "description": "Plant Genomics Benchmark - Plant-specific genomic understanding tasks",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  }
+}

datasets/datasets_info.json

@@ -0,0 +1,52 @@
+{
+  "deepsea_tfb_prediction": {
+    "filename": "deepsea_tfb_prediction.zip",
+    "genome": "DNA",
+    "species": "human",
+    "task_type": "sequence_classification",
+    "description": "DeepSEA Transcription Factor Binding prediction dataset",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "translation_efficiency_prediction": {
+    "filename": "translation_efficiency_prediction.zip",
+    "genome": "RNA",
+    "species": "multi-species",
+    "task_type": "regression",
+    "description": "Translation efficiency prediction for mRNA sequences",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "variant_effect_prediction": {
+    "filename": "variant_effect_prediction.zip",
+    "genome": "DNA",
+    "species": "human",
+    "task_type": "sequence_classification",
+    "description": "Variant effect prediction for genomic variants",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "RNA-SSP-Archive2": {
+    "filename": "RNA-SSP-Archive2.zip",
+    "genome": "RNA",
+    "species": "multi-species",
+    "task_type": "token_classification",
+    "description": "RNA Secondary Structure Prediction - Archive 2 dataset",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  },
+  "RNA-mRNA": {
+    "filename": "RNA-mRNA.zip",
+    "genome": "RNA",
+    "species": "multi-species",
+    "task_type": "sequence_classification",
+    "description": "RNA mRNA classification dataset",
+    "url": "https://huggingface.co/datasets/yangheng/OmniGenBench_Hub",
+    "author": "YANG, HENG",
+    "license": "Apache-2.0"
+  }
+}

models/models_info.json

@@ -0,0 +1,4 @@
+{
+  "models": {},
+  "note": "Model files will be added in future releases. Currently, pre-trained models are hosted directly on HuggingFace Hub under yangheng/ namespace (e.g., yangheng/OmniGenome-186M, yangheng/PlantRNA-FM)."
+}

pipelines/pipelines_info.json

@@ -0,0 +1,4 @@
+{
+  "pipelines": {},
+  "note": "Pipeline files will be added in future releases."
+}