AI_Genomics Project

This repository contains the implementation and integration of two powerful genomics models: GET (Gene Expression Transformer) and AlphaFold.

Project Structure

AI_Genomics/
├── models/
│   ├── get_model/          # GET model implementation (175MB)
│   │   ├── tutorials/      # Jupyter notebooks for data processing and model usage
│   │   │   ├── prepare_pbmc.ipynb     # Data processing tutorial
│   │   │   ├── finetune_pbmc.ipynb    # Model fine-tuning tutorial
│   │   │   ├── predict_atac.ipynb     # ATAC prediction demo
│   │   │   └── pretrain_pbmc.ipynb    # Pre-training tutorial
│   │   ├── get_model/     # Core model implementation
│   │   └── env.yml        # Conda environment specification
│   └── alphafold/         # AlphaFold implementation (34MB)
│       ├── data/          # Symbolic link to alphafold_data
│       ├── configs/       # Model configurations
│       └── checkpoints/   # Model checkpoints
├── experiments/
│   ├── get_experiments/   # GET experiment scripts and results
│   └── af_experiments/    # AlphaFold experiment scripts and results
├── utils/                 # Shared utility functions
├── notebooks/            # Jupyter notebooks for analysis
└── docs/                 # Documentation and model mindmaps

Model Data Locations

AlphaFold Data

Required data includes:
- Sequence databases (UniRef90, BFD, MGnify)
- Structure templates (PDB70)
- Parameter files
- Model weights

Note: AlphaFold data setup will be done separately following the official installation guide.

GET Model Data

The GET model requires the following data preparation steps:
1. PBMC Data Processing:
- Follow the tutorial in models/get_model/tutorials/prepare_pbmc.ipynb
- Data processing pipeline includes:
- Peak sorting (chr1, chr2, chr3 order)
- Count matrix preparation
- Quality checks (>3M depth recommended)

1. Model Training Data:
2. Fine-tuning data: Follow models/get_model/tutorials/finetune_pbmc.ipynb
3. ATAC prediction: Use models/get_model/tutorials/predict_atac.ipynb
4. Pre-training: Reference models/get_model/tutorials/pretrain_pbmc.ipynb

Setup and Installation

1. Clone the repository:

git clone https://github.com/[your-username]/AI_Genomics.git /home/caom/AI_Genomics
cd /home/caom/AI_Genomics

1. Create and activate a conda environment:

conda create -n ai_genomics python=3.8
conda activate ai_genomics

1. Install dependencies:

pip install -r requirements.txt

1. Set up model-specific requirements:
2. GET Model:
   bash cd models/get_model conda env create -f env.yml conda activate get

3. AlphaFold:
   ```bash
   # Create AlphaFold conda environment
   cd models/alphafold
   conda create -n alphafold python=3.10
   conda activate alphafold

   # Install JAX with CUDA support
   pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

   # Install other dependencies
   conda install -y -c conda-forge openmm=7.5.1 pdbfixer
   conda install -y -c bioconda hmmer hhsuite kalign2
   pip install -r docker/requirements.txt

   # Download genetic databases and model parameters
   # (This will be done separately following cluster-specific storage guidelines)
   ```

   Note: We use conda environment instead of Docker in the cluster environment for:
   - Better integration with SLURM job scheduler
   - Direct access to cluster's optimized CUDA libraries
   - Improved performance without Docker virtualization
   - Better resource management
   - Direct access to cluster storage

Computational Resources

GPU Access

To access GPU resources for model training and inference, use the following SLURM command:

srun -p general --pty -t 120:00 --cpus-per-task=32 --mem=64G --gres=gpu:a100:2 /bin/bash

This command requests:
- Partition: general
- Time limit: 120 hours
- CPUs: 32 cores
- Memory: 64GB
- GPUs: 2 NVIDIA A100 GPUs
- Interactive bash session

Note: Adjust the resources (time, CPU, memory, GPU) based on your specific needs.

Version Control

This repository uses Git for version control. Important files:
- .gitignore: Excludes large data files, model checkpoints, and environment-specific files
- .gitattributes: Handles large file storage using Git LFS
- requirements.txt: Lists all Python dependencies

Contributing

1. Create a new branch for your feature
2. Make your changes
3. Submit a pull request

License

Please refer to the original licenses of AlphaFold and GET models.