Name: CRISPR Prime Editing for Genetic Disease: AI-Based pegRNA Design, Variant Correction & Outcome Prediction
Start: 2026-08-31
End: 2026-09-02

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About Workshop

This three-day workshop introduces participants to AI-guided prime editing for precision genome engineering, from disease-variant interpretation to pegRNA design and outcome prediction. Participants will learn how to identify clinically relevant variants, retrieve genomic context, design pegRNAs, compare PBS and RTT configurations, evaluate PE3 and twinPE strategies, and prioritise candidate correction designs using computational tools such as ClinVar, Ensembl, VEP, OptiPrime, Python, and Google Colab.

Aim

To provide participants with practical knowledge of disease-variant interpretation, AI-assisted pegRNA design, prime-editing outcome prediction, and candidate optimisation for precision genome-engineering applications.

What Participants Will Learn

Understand the principles of prime editing and its role in precision genome engineering.
Explore clinically relevant variants, transcripts, genomic coordinates, and sequence context using genomic databases.
Design candidate pegRNAs by evaluating spacer, PBS, RTT, PE3, and paired-editing configurations.
Analyse predicted prime-editing efficiency and potential editing outcomes using AI-based tools.
Develop a prioritised variant-correction strategy based on editing performance, sequence context, and design quality.

Structure

Day 1: Disease Variant Interpretation & Prime-Editing Strategy

Core Objective: Identify a clinically relevant genetic variant, interpret its genomic and transcript context, and define an appropriate prime-editing correction strategy.

Understand prime editing, precision genome engineering, and key differences between prime editing and conventional CRISPR-Cas approaches.
Identify disease-associated variants, genomic coordinates, transcripts, alleles and functional consequences using clinical and genomic databases.
Retrieve the local sequence context, assess variant pathogenicity, and define a preliminary sequence-level correction strategy for prime editing.

Hands-on Lab: Select a clinically relevant variant from ClinVar, identify its gene, transcript and genomic position, annotate the variant using Ensembl VEP, retrieve the surrounding genomic sequence, and prepare a preliminary prime-editing correction strategy.

Tools Covered: ClinVar, Ensembl, VEP, UCSC Genome Browser, NCBI Output: Annotated disease variant, reference transcript, genomic sequence context and preliminary prime-editing correction strategy.

Day 2: AI-Guided pegRNA Design & Prime-Editing Outcome Prediction

Core Objective: Design and compare candidate pegRNAs and use AI-based prediction to evaluate prime-editing efficiency and alternative editing strategies.

Understand pegRNA architecture, including spacer sequence, primer-binding site (PBS) and reverse-transcription template (RTT), and their roles in editing performance.
Generate and compare multiple pegRNA configurations while considering sequence context, PE2, PE3, nicking-guide and paired pegRNA strategies.
Apply AI-guided prediction to compare editing efficiencies and prioritise promising prime-editing designs, including PE3 and twinPE approaches where applicable.

Hands-on Lab: Generate candidate pegRNA designs for the Day 1 variant, compare alternative PBS and RTT configurations, evaluate predicted editing efficiencies, assess PE3 or paired-design strategies where appropriate, and shortlist high-priority candidates.

Tools Covered: OptiPrime, Python, Google Colab, sequence-analysis tools Output: Candidate pegRNA designs with PBS/RTT configurations, predicted editing efficiencies and a prioritised shortlist of prime-editing strategies.

Day 3: Prime-Editing Optimisation, Variant-Correction Ranking & Design Reporting

Core Objective: Integrate predicted editing performance, sequence-context considerations and potential unwanted outcomes to prioritise a final precision-genome-editing design.

Compare candidate designs using predicted editing efficiency, sequence context, editing configuration and intended-versus-unwanted outcome considerations.
Interpret PE3 and twinPE predictions, evaluate design-quality parameters, and perform multi-criteria ranking of candidate pegRNAs.
Assess variant-correction feasibility, recognise limitations of computational predictions, and develop a research-oriented prime-editing design report.

Hands-on Lab: Compare predicted outcomes across shortlisted designs, evaluate editing efficiency and potential unwanted-edit considerations, rank pegRNAs using multiple criteria, select a preferred correction strategy, and prepare a final AI-guided prime-editing design report.

Tools Covered: OptiPrime, Python, Google Colab, sequence-analysis tools, candidate-ranking templates Output: Ranked pegRNA candidates, preferred variant-correction strategy, predicted editing-performance summary and final precision-genome-engineering design report.

Important Dates

Registration Ends

4: 30 PM

Workshop Dates

2026-08-31

5:30 PM

What You Will Gain

Outcomes

Identify and interpret disease-associated variants using clinical and genomic databases.
Retrieve reference transcripts and sequence context required for prime-editing design.
Generate and compare candidate pegRNA designs with different PBS and RTT configurations.
Evaluate predicted editing efficiency and compare PE2, PE3, and twinPE strategies where applicable.
Rank candidate correction designs and prepare a preliminary AI-guided prime-editing design report.

Who Should Attend

Graduate and postgraduate students in biotechnology, genetics, molecular biology, bioinformatics, biomedical sciences, and life sciences
PhD scholars and research fellows
Academicians and faculty members
Genome-editing and molecular-biology researchers
Bioinformatics and computational-biology professionals
Biotechnology and pharmaceutical industry professionals
Researchers working in gene therapy, genetic disorders, functional genomics, or precision medicine