
AI-Driven CRISPR Guide RNA Design & Off-Target Prediction
Optimizing Gene Editing Precision with AI-Driven CRISPR Guide RNA Design.
Skills you will gain:
About Program:
CRISPR-Cas9 has revolutionized genetic engineering, enabling precise editing of DNA. However, one of the challenges in using CRISPR is the potential for off-target effects that can lead to unintended genetic modifications. This workshop focuses on leveraging AI to design optimized guide RNAs that improve the specificity and efficiency of CRISPR-based genome editing. By integrating machine learning algorithms and large-scale genomic data, participants will learn to predict and minimize off-target effects in gene editing experiments.
Over three days, the workshop will cover the fundamentals of CRISPR-Cas9 technology, the design of gRNAs using AI, and methods to predict and validate off-target effects. Hands-on sessions will allow participants to practice designing guide RNAs and running prediction models for off-target identification using AI-based tools.
Aim: This workshop aims to explore the application of AI in CRISPR-Cas9 technology, focusing on guide RNA (gRNA) design and off-target prediction. Participants will learn how to utilize AI algorithms to optimize gRNA sequences for precise gene editing and predict potential off-target effects, ensuring the accuracy and efficiency of CRISPR-based therapies.
Program Objectives:
- Understand the fundamentals of CRISPR-Cas9 and its applications in gene editing.
- Learn how AI can be applied to optimize guide RNA design for CRISPR.
- Gain hands-on experience in using AI algorithms to predict off-target effects.
- Explore methods for validating and minimizing off-target effects in CRISPR experiments.
- Understand the implications of off-target effects and how to mitigate them in genome editing.
What you will learn?
Day 1 – CRISPR Fundamentals & AI Opportunities
- CRISPR-Cas9 mechanism and applications
- Guide RNA selection criteria (efficiency, specificity)
- AI’s role in gRNA prediction and off-target scoring
- Case study: DeepCRISPR, CRISPR-Net tools
Day 2 – Practical AI Models for gRNA Design
- Input datasets for AI models (genomic sequences, PAM sites)
- Using AI tools for scoring potential gRNAs
- Live demo: Running a gRNA design using web-based AI platforms
- Limitations and accuracy considerations
Day 3 – From In-Silico Prediction to Lab Validation
- Integrating AI predictions into wet-lab workflows
- Reducing false positives in off-target effects
- Applications in therapeutic genome editing
Capstone Discussion: Designing a CRISPR experiment with AI support
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
- Undergraduate/postgraduate degree in Genetics, Biotechnology, Bioinformatics, Molecular Biology, or related fields.
- Professionals in genomic research, molecular biology, gene therapy, and genetic engineering.
- Data scientists and AI/ML engineers interested in applying AI to genetic editing technologies.
- Individuals with a strong interest in CRISPR technology and gene editing applications.
Career Supporting Skills
Program Outcomes
- Knowledge of the CRISPR-Cas9 technology and its applications in gene editing.
- Skills in designing optimized guide RNAs using AI-based tools.
- Ability to predict and assess off-target effects in CRISPR-based experiments.
- Practical experience with predictive modeling and off-target identification methods.
- Understanding of how to minimize off-target effects and increase the precision of gene editing.
