Design and score CRISPR guide RNAs for Cas9 and Cas12a nucleases using on-target activity prediction models.
pip install biopython crisprscanTell your AI agent what you want to do:
- "Design guides to knock out BRCA1"
- "Find sgRNAs targeting exon 3 of TP53 with high activity scores"
- "Design Cas12a guides for this target sequence"
"Design the best 3 guides to knock out the KRAS gene"
"Find sgRNAs in the first coding exon of MYC"
"Score this guide sequence for on-target activity: ATCGATCGATCGATCGATCG"
"Design guides within 100bp of position chr7:140453136"
"Design Cas12a guides for my target region"
"Find SaCas9-compatible guides for AAV delivery"
- Retrieve or accept the target gene/sequence
- Identify all valid PAM sites in the region
- Extract guide sequences for each PAM
- Score guides using activity prediction models
- Filter by GC content and avoid poly-T stretches
- Return top-ranked guides with scores and positions
- Exon targeting - Target early coding exons for reliable knockout
- GC content - Optimal range is 40-70%; outside this reduces activity
- Avoid poly-T - Four or more consecutive T's terminate transcription
- Multiple guides - Design 3-5 guides per gene for redundancy
- Off-target check - Always check off-targets before ordering guides