DGD-Cas9 — Deep Guide Designer for CRISPR-Cas9

This branch (main) is a lightweight landing page: it shows the Snakemake workflow and a small demo input so you can see how the pipeline is organized, without cloning the full source tree, trained models, or PDB-scale data.

This branch is not runnable on its own — the Snakefile here calls into Python pipeline scripts (DGD.py, Accessory/*.py, etc.) that intentionally live on the full-pipeline branch instead, to keep main small and easy to browse.

To actually run the pipeline

git clone https://github.com/GuideDesigner/DGD-Cas9.git
cd DGD-Cas9
git checkout full-pipeline
pip install -r requirements.txt
pip install snakemake "pulp<2.8"
snakemake --cores 1 -n     # preview the workflow
snakemake --cores 1        # run it against demo/input.fa

See the full-pipeline branch's README for full installation instructions, the 12-step pipeline description, the three pipeline variants (standard SpCas9, broad-PAM Cas9 variants, base editors), known issues, and CI/CD details.

What's here

main/
├── Snakefile              # The real workflow definition (same file as full-pipeline)
├── config/config.yaml     # Sample/variant configuration
├── envs/environment.yaml  # Conda environment for --use-conda
└── demo/input.fa          # Small synthetic FASTA for trying the workflow

Overview

DGD-Cas9 is a deep-learning pipeline for designing and scoring CRISPR-Cas9 sgRNA guide candidates. It integrates sequence features, RNA secondary structure, and spacer–scaffold base-pairing connectivity into a CNN ensemble model to predict guide activity.

Authors: Vipin Menon, Jang-il Sohn, Seokju Park, Jin-Wu Nam Lab: Bioinformatics & Genomics Lab (BIG Lab), Hanyang University, Seoul 04763, Korea Contact: a.vipin.menon@gmail.com | jwnam@hanyang.ac.kr

License

MIT License — see the full-pipeline branch's LICENSE file for details.