CRISPR sgRNA Design Tool

A Python-based tool for designing and evaluating CRISPR guide RNAs (sgRNAs) with a focus on longevity-related genes.

Features

• Fetch gene sequences from NCBI using Biopython
• Design candidate sgRNAs with NGG PAM sequences
• Score sgRNAs based on multiple criteria:
  • GC content optimization (40-60%)
  • Homopolymer detection and penalization
  • Off-target prediction using BLAST
  • Position-based efficiency scoring
• Visualization of sgRNA scores and rankings
• Comparative analysis with existing tools

Installation

1. Clone this repository:

git clone https://github.com/yourusername/crispr_design.git
cd crispr_design

2. Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Project Structure

crispr_design/
├── src/                 # Source code
│   ├── sequence.py      # Gene sequence retrieval
│   ├── design.py        # sgRNA design algorithms
│   ├── scoring.py       # Scoring functions
│   └── visualization.py # Data visualization
├── tests/              # Unit tests
├── data/              # Gene sequences and results
├── notebooks/         # Jupyter notebooks for analysis
└── requirements.txt   # Project dependencies

Example Results

The examples/ directory contains sample outputs from analyzing key longevity-associated genes:

FOXO3

• foxo3_candidates.txt: List of candidate sgRNAs with their scores and properties
• foxo3_gc_distribution.png: Visualization of GC content distribution across candidate sgRNAs

APOE

• apoe_candidates.txt: List of candidate sgRNAs with their scores and properties
• apoe_gc_distribution.png: Visualization of GC content distribution across candidate sgRNAs

SIRT1

• sirt1_candidates.txt: List of candidate sgRNAs with their scores and properties
• sirt1_gc_distribution.png: Visualization of GC content distribution across candidate sgRNAs

Usage

1. Set your NCBI email in the configuration:

from Bio import Entrez
Entrez.email = "your.email@example.com"  # Required for NCBI data access

2. Basic sgRNA design workflow:

from src.sequence import fetch_gene_sequence
from src.design import find_sgrnas
from src.scoring import score_sgrna

# Example using FOXO3
sequence = fetch_gene_sequence("NM_001455.4")  # FOXO3 gene
# Or APOE: NM_000041.4
# Or SIRT1: NM_012238.5

# Find candidate sgRNAs
candidates = find_sgrnas(sequence)

# Score candidates
scores = [score_sgrna(sg) for sg in candidates]

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• NCBI for providing gene sequence data
• Biopython community for their excellent tools
• CRISPR research community for scoring guidelines