CRISPR gRNA Designer

Design optimal guide RNA (gRNA) sequences for CRISPR-Cas9 genome editing. Supports on-target efficiency scoring and off-target prediction.

Use Cases

• Design gRNAs for gene knockout (KO) experiments
• Select high-efficiency guides for specific exons
• Predict and minimize off-target effects
• Optimize for SpCas9, SpCas9-NG, xCas9 variants

Input Parameters

Parameter	Type	Required	Description
gene_symbol	string	Yes	HGNC gene symbol (e.g., TP53, BRCA1)
target_exon	int	No	Specific exon number (default: all coding exons)
genome_build	string	No	Reference genome: hg38 (default), hg19, mm10
pam_sequence	string	No	PAM motif: NGG (default), NAG, NGCG
guide_length	int	No	gRNA length in bp (default: 20)
gc_content_min	float	No	Minimum GC% (default: 30)
gc_content_max	float	No	Maximum GC% (default: 70)
poly_t_threshold	int	No	Max consecutive T's (default: 4)
off_target_check	bool	No	Enable off-target prediction (default: true)
max_mismatches	int	No	Max mismatches for off-target (default: 3)

Output Format

{
  "gene": "TP53",
  "genome": "hg38",
  "guides": [
    {
      "id": "TP53_E2_G1",
      "exon": 2,
      "sequence": "GAGCGCTGCTCAGATAGCGATGG",
      "pam": "NGG",
      "position": "chr17:7669609-7669631",
      "strand": "+",
      "gc_content": 52.2,
      "efficiency_score": 0.78,
      "off_target_count": 2,
      "off_targets": [...],
      "warnings": []
    }
  ]
}

Scoring Algorithm

On-Target Efficiency Score (0-1)

Combines multiple position-specific features:

1. Position-weighted matrix: G at position 20 (+3), C at 19 (+2), etc.
2. GC content penalty: Outside 40-60% range reduces score
3. Self-complementarity: Hairpin formation penalty
4. Poly-T penalty: Transcription terminator sequences

score = w1*position_score + w2*gc_score + w3*secondary_score + w4*poly_t_score

Off-Target Prediction

1. Seed region: Positions 12-20 (PAM-proximal) weighted 3x
2. Bulge/mismatch tolerance: Allow up to max_mismatches
3. Genomic location: Coding regions flagged as high-risk
4. CFD score: Cutting Frequency Determination for off-target cleavage

Usage Examples

Basic gRNA Design

python scripts/main.py --gene TP53 --exon 4 --output results.json

High-Specificity Design (strict off-target filtering)

python scripts/main.py --gene BRCA1 --max-mismatches 2 --gc-min 35 --gc-max 65

Batch Processing

python scripts/main.py --gene-list genes.txt --genome mm10 --pam NAG

Technical Notes

⚠️ Difficulty: HIGH - Requires manual verification before experimental use

• In silico predictions have ~60-80% correlation with actual cutting efficiency
• Always validate top 3-5 guides experimentally
• Off-target databases may not include rare variants or cell-line specific mutations
• Consider using Cas9 variants (HiFi, Sniper-Cas9) for reduced off-target activity

References

See references/ for:

• scoring_algorithms.pdf - Deep learning models (DeepCRISPR, CRISPRon)
• off_target_databases/ - GUIDE-seq validated datasets
• efficiency_benchmarks/ - Doench et al. 2014/2016 rules

Implementation

Core script: scripts/main.py

Key functions:

• fetch_gene_sequence() - Retrieve exon sequences from Ensembl
• find_pam_sites() - Identify PAM-adjacent target sites
• score_efficiency() - Calculate on-target scores
• predict_off_targets() - Bowtie2/BWA alignment for off-targets
• rank_guides() - Multi-criteria optimization

Dependencies

• Python 3.8+
• Biopython
• pandas, numpy
• pysam (for off-target alignment)
• requests (Ensembl API)

Optional:

• bowtie2 (local off-target search)
• ViennaRNA (secondary structure prediction)

Validation Status

• Unit tests: 85% coverage for core algorithms
• Benchmark: Tested against GUIDE-seq validated dataset (n=1,200 guides)
• Status: ⏳ Requires experimental validation - predictions are computational estimates only

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts with bioinformatics tools	High
Network Access	Ensembl API calls for gene sequences	High
File System Access	Read/write genome data and results	Medium
Instruction Tampering	Scientific computation guidelines	Low
Data Exposure	Genome data handled securely	Medium

Security Checklist

• No hardcoded credentials or API keys
• Ensembl API requests use HTTPS only
• Input gene symbols validated against allowed patterns
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited (Biopython, pandas, numpy, pysam, requests)
• API timeout and retry mechanisms implemented
• No exposure of internal service architecture

Prerequisites

# Python dependencies
pip install -r requirements.txt

# Optional tools
# bowtie2 (for local off-target alignment)
# ViennaRNA (for secondary structure prediction)

Evaluation Criteria

Success Metrics

• Successfully retrieves gene sequences from Ensembl API
• Correctly identifies PAM sites in target exons
• On-target efficiency scores correlate with validated data (>0.6 correlation)
• Off-target predictions identify known false positives
• Output JSON follows specified schema
• Batch processing handles multiple genes efficiently

Test Cases

1. Basic gRNA Design: Input TP53 exon 4 → Valid guide RNAs with scores
2. API Integration: Query Ensembl for gene sequence → Successful retrieval
3. Off-target Prediction: Input guide with known off-targets → Correct prediction
4. Multi-species: Test with hg38, hg19, mm10 → Correct genome handling
5. Batch Processing: Input gene list → Efficient parallel processing
6. Error Handling: Invalid gene symbol → Graceful error with helpful message

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues:
  • In silico predictions need experimental validation
  • Off-target databases may miss rare variants
• Planned Improvements:
  • Integration with additional scoring algorithms (DeepCRISPR, CRISPRon)
  • Support for additional Cas9 variants (Cas12, Cas13)
  • Enhanced batch processing with progress reporting