CRISPR & Gene Editing Design
CHOPCHOP CRISPR Guide RNA Design Tool
by Montague Lab, University of Bergen (Academic)
Fast, flexible web tool for CRISPR guide RNA design across all editing technologies
Category
CRISPR & Gene Editing Design
Founded
2014
Headquarters
Bergen, Norway
Overview
CHOPCHOP is a free, open-source web application developed by the Montague laboratory at the University of Bergen that enables researchers to design guide RNAs for CRISPR-Cas9, Cas12a, Cas13, CRISPR activation, CRISPR interference, and base editing experiments. The tool accepts gene names, transcript IDs, or custom sequences and returns ranked guide RNAs with efficiency scores, GC content, secondary structure predictions, and off-target analysis. CHOPCHOP supports a wide range of organisms and continuously adds new reference genomes based on community requests. Graduate students, postdoctoral researchers, and core facility staff at universities and research institutes worldwide use CHOPCHOP to rapidly design CRISPR experiments. The tool's support for multiple Cas enzymes and editing modalities — including prime editing and base editing — makes it a versatile first-stop resource when planning any CRISPR-based perturbation experiment, regardless of the cell type or organism being studied. CHOPCHOP distinguishes itself through its breadth of supported CRISPR applications, including unique support for newer editing technologies as they emerge. The tool generates publication-ready figures showing guide RNA positions on annotated gene diagrams, simplifying the methods figure preparation for research papers. Its open-source codebase allows institutional bioinformatics teams to deploy and customize local instances for high-throughput or sensitive genomic work.
Key Features
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.
Multi-Editor Support
Design tools for CRISPR-Cas9, Cas12, base editing, and prime editing systems.
Pros & Cons
Pros
- +Cloud-based design tools enable collaborative gene editing project management across teams
- +Multi-editor support covers CRISPR-Cas9, Cas12, base editing, and prime editing systems
- +Comprehensive off-target prediction algorithms evaluate billions of potential cleavage sites
- +AI-optimized guide RNA design maximizes on-target efficiency while minimizing off-target effects
- +Regulatory-ready documentation packages support IND applications for gene therapy programs
- +Integration with delivery system optimization (viral vectors, LNPs, electroporation)
Cons
- −Delivery challenges limit efficient CRISPR component delivery to many tissue types in vivo
- −Off-target editing effects remain a safety concern especially for therapeutic applications
- −Regulatory pathways for gene-edited therapies are evolving and differ across jurisdictions
- −Editing efficiency varies significantly across cell types and genomic loci
- −Intellectual property landscape for CRISPR technology is complex with multiple competing patents
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.