Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Driven CRISPR Guide RNA Design and Off-Target Prediction sits where genome editing meets machine learning. You learn how guide-RNA choice determines editing success, then how computational and machine-learning models predict the two things that matter most: on-target cutting efficiency and off-target risk elsewhere in the genome. The course covers the features that drive these models, the main prediction tools, and how to design and rank guides for a target with confidence. You finish able to run an AI-assisted guide-design workflow and interpret its predictions critically. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to CRISPR guide-RNA design — using machine-learning models to predict on-target efficiency and off-target risk for reliable genome editing.
📋 Course Objectives
1. Explain how guide-RNA design affects editing outcomes.
2. Use ML models to predict on-target efficiency.
3. Predict and rank off-target risk across the genome.
4. Interpret the features driving guide-design models.
5. Design and prioritise guides for a chosen target.
👥 Who Should Enroll?
• Genome-editing and molecular-biology researchers
• Bioinformatics scientists in functional genomics
• Biotech R&D professionals
• Students specialising in CRISPR technology
🚀 Key Learning Outcomes
• The ability to design guides with AI-assisted prediction.
• A guide-design and off-target analysis workflow.
• A foundation in computational genome editing.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
CRISPR Fundamentals & AI Opportunities
Exploring CRISPR-Cas9 mechanism and applications • Defining guide RNA selection criteria for efficiency and specificity • Introducing AI’s role in gRNA prediction and off-target scoring
Practical AI Models for gRNA Design
Preparing input datasets for AI models • Utilizing AI tools for scoring potential gRNAs • Conducting live demos of gRNA design using web-based AI platforms
From In-Silico Prediction to Lab Validation
Integrating AI predictions into wet-lab workflows • Reducing false positives in off-target effects • Applying AI-driven CRISPR in therapeutic genome editing
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | DeepCRISPR |
| Covered Tool / Platform | CRISPR-Net |
| Covered Tool / Platform | web-based AI platforms |
Frequently Asked Questions
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