CRISPR & Gene Editing Design
Cas-OFFinder Off-Target Prediction Tool
by Kim Lab, Institute for Basic Science (Academic)
Fast, GPU-accelerated CRISPR off-target site identification across any genome
Category
CRISPR & Gene Editing Design
Founded
2014
Headquarters
Daejeon, South Korea
Overview
Cas-OFFinder is an open-source algorithm developed by Jin-soo Kim's laboratory at the Institute for Basic Science in South Korea that efficiently searches genomic sequences for potential off-target sites of CRISPR-Cas nucleases. The tool accepts a guide RNA sequence and PAM type as inputs and uses GPU acceleration (via OpenCL) to rapidly scan entire genomes for sequences matching the guide with up to a user-defined number of mismatches and DNA/RNA bulges. Cas-OFFinder supports all known Cas9 and Cpf1 PAM sequences and can search any genome for which a FASTA reference sequence is available. Researchers designing CRISPR therapeutics, developing CRISPR screening libraries, or characterizing guide RNA specificity for publication use Cas-OFFinder to generate comprehensive lists of potential off-target sites that require validation. In therapeutic development contexts, identifying all high-similarity off-target sites is a regulatory requirement, and Cas-OFFinder's speed and flexibility — including support for non-standard PAMs and bulge-containing mismatches — make it suitable for both research and regulatory submissions. Cas-OFFinder is freely available as a web service on the RGENOME platform and as a downloadable command-line tool for local, large-scale off-target analysis. Its GPU-based acceleration enables whole-genome off-target searches that would require hours on CPU-only implementations to complete in minutes, making it practical for high-throughput guide RNA screening workflows. The tool has been cited in hundreds of publications and is integrated into several commercial CRISPR design pipelines as a backend off-target prediction engine.
Key Features
Editing Efficiency Prediction
ML models predict editing efficiency for specific guide-target combinations across cell types.
Regulatory Documentation
Automated generation of regulatory-ready documentation packages for gene therapy IND applications.
Collaborative Project Management
Cloud-based tools for team collaboration on gene editing projects with version control.
AI-Optimized Guide RNA Design
Machine learning algorithms maximize on-target efficiency while minimizing off-target effects.
Off-Target Prediction
Comprehensive algorithms evaluate billions of potential off-target cleavage sites genome-wide.
Pros & Cons
Pros
- +Cloud-based design tools enable collaborative gene editing project management across teams
- +Pre-validated guide libraries for common model organisms accelerate experimental design
- +Integration with delivery system optimization (viral vectors, LNPs, electroporation)
- +Regulatory-ready documentation packages support IND applications for gene therapy programs
- +AI-optimized guide RNA design maximizes on-target efficiency while minimizing off-target effects
- +Comprehensive off-target prediction algorithms evaluate billions of potential cleavage sites
- +Multi-editor support covers CRISPR-Cas9, Cas12, base editing, and prime editing systems
Cons
- −Delivery challenges limit efficient CRISPR component delivery to many tissue types in vivo
- −Intellectual property landscape for CRISPR technology is complex with multiple competing patents
- −Editing efficiency varies significantly across cell types and genomic loci
- −Regulatory pathways for gene-edited therapies are evolving and differ across jurisdictions
Use Cases
Research Workflow Optimization
AI-powered optimization of research workflows to accelerate discovery timelines and improve reproducibility.
Data Analysis & Insights
Machine learning analysis of complex biological datasets to extract actionable insights and identify patterns.
Collaboration & Knowledge Management
Platform-enabled collaboration across distributed research teams with integrated data sharing and knowledge capture.