Project scaffold for genomic notebooks and analysis, managed with uv.

1. Install uv (one-time):

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Create the environment and install deps:

uv sync

3. Register a Jupyter kernel for this project (optional but recommended):

uv run python -m ipykernel install --user --name rogen-aging --display-name "Python (rogen-aging)"

4. Launch JupyterLab and start working in notebooks/:

uv run jupyter lab

• Add a runtime dependency:

uv add <package>

• Add a dev-only dependency (linters, tests, etc.):

uv add --dev <package>

• Install optional genomics extras:

uv add .[genomics]

• src/rogen_aging/: Python package for shared code
• notebooks/: genomic analysis notebooks
• data/: put large/local data here (git-ignored)

This project targets Python 3.12 (configured in pyproject.toml).

The notebooks/AlphaGenome.ipynb notebook performs a comprehensive analysis of gene lists for Alzheimer's and Parkinson's diseases. To run it, you'll need to set up your environment with the necessary API keys and data files.

The notebook requires API keys for NCBI and AlphaGenome. Follow these steps to set them up:

1. Copy the .env.example file to .env:

cp .env.example .env

2. Edit the .env file and add your actual API keys:

NCBI_API_KEY=your_actual_ncbi_api_key_here
ALPHA_GENOME_API_KEY=your_actual_alpha_genome_api_key_here

Important: The .env file is git-ignored and will not be committed to version control, keeping your API keys secure.

The notebook needs two data files:

• Supplementary Table 3.xlsx: An Excel file with gene lists.
• longevitymap.sqlite: A SQLite database from the Longevity Map.

You should place these files in the data/ directory. This directory is included in .gitignore to prevent large data files from being committed to your repository.

Once you've set up your API keys and data files, you can launch JupyterLab:

uv run jupyter lab

Now, you can open notebooks/AlphaGenome.ipynb and run the cells. The notebook is configured to read the data files from the data/ directory and the API keys from your environment.

This repository includes a complete methylation calling pipeline for Oxford Nanopore sequencing data.

• METHYLATION_PIPELINE_README.md - Comprehensive user guide
• docs/METHYLATION_PIPELINE_USAGE.md - Detailed step-by-step usage guide
• Pipeline Scripts:
  • pipeline_validation.sh - Basecalling and methylation extraction
  • downstream_analysis.R - DMR calling and analysis
  • notebooks/DownstreamMethylationAnalysis.ipynb - Interactive R notebook

The methylation pipeline integrates three main tools:

1. Dorado - Basecalling with methylation-aware models
2. Modkit - BAM to bedMethyl conversion
3. DMRcaller - Differential methylation analysis

# 1. Run basecalling and methylation extraction
./pipeline_validation.sh

# 2. Run downstream analysis
Rscript downstream_analysis.R

# Or use the interactive notebook
uv run jupyter lab
# Open: notebooks/DownstreamMethylationAnalysis.ipynb

For detailed instructions, see METHYLATION_PIPELINE_README.md.