CRISPR Informatics: Designing & Analyzing Gene Edits- recorded courses
Master the computational precision of Cas9 technology by designing high-efficiency gRNAs and predicting off-target effects with AI-driven modeling. Bridge the gap between digital design and genomic reality with cutting-edge bioinformatics pipelines for CRISPR-Cas9, Cas12, and Prime Editing.
Course Description
This advanced CRISPR Informatics program is engineered for the 2026 genomic frontier, where Gene Editing meets Artificial Intelligence. You will move beyond basic "cut and paste" biology to master the sophisticated computational tools required for Precision Genome Engineering. This course provides deep-dive training in gRNA design, utilizing Machine Learning algorithms to predict on-target efficiency and minimize hazardous off-target effects. You will learn to navigate the complex landscape of NGS-based CRISPR analysis, using automated pipelines to quantify indel frequencies and HDR (Homology-Directed Repair) efficiency. By integrating Structural Bioinformatics with Deep Learning models, you will gain the technical expertise to design edits for rare diseases, agricultural biotechnology, and therapeutic development. Become a specialist in the "Dry Lab" side of CRISPR, where every successful edit begins with a perfectly calculated digital blueprint.
What You'll Learn
Computational gRNA Design: Utilize AI-powered software to select optimal target sites with high specificity and low toxicity.
Off-Target Risk Assessment: Master the use of Cas-OFFinder and GUIDE-seq data to predict and validate unintended genomic alterations.
Quantifying Edit Success: Build Bioinformatics pipelines to analyze Amplicon Sequencing data and calculate CRISPR efficiency.
Advanced Editing Modalities: Design strategies for Base Editing and Prime Editing using specialized computational frameworks.
AI in Gene Editing: Explore how Deep Neural Networks are being used to predict DNA repair outcomes (NHEJ vs. HDR) before the experiment begins.
Curriculum
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Computational gRNA Design: Utilize AI-powered software to select optimal target sites with high specificity and low toxicity.
Lesson -
Off-Target Risk Assessment: Master the use of Cas-OFFinder and GUIDE-seq data to predict and validate unintended genomic alterations.
Lesson -
Quantifying Edit Success: Build Bioinformatics pipelines to analyze Amplicon Sequencing data and calculate CRISPR efficiency.
Lesson -
Advanced Editing Modalities: Design strategies for Base Editing and Prime Editing using specialized computational frameworks.
Lesson -
AI in Gene Editing: Explore how Deep Neural Networks are being used to predict DNA repair outcomes (NHEJ vs. HDR) before the experiment begins.
Lesson
