Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning - Dissertation

Aug 5, 2026

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Genome editing enables precise modification of genetic material, supporting advances in life science research and genetic disease understanding. Prime editing is highlighted for its capacity to edit genomes while reducing double-strand breaks common to conventional CRISPR-Cas9. This dissertation develops machine-learning-driven strategies to improve prime editing efficiency by analyzing two determinants: pegRNA design and local chromatin characteristics. Two comprehensive studies evaluate guide RNA sequence-context effects and edit-type and chromatin-context generalization, grounded in large-scale high-throughput screening data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages159

Reading Time6 h 41 min

AccessFree to download

Summary

Acknowledgments

List of Abbreviations

Preface

Introduction

The rise of genome editing

Cas9 nuclease editing

Base editing

Prime editing

Effect of chromatin on gene editing

High-throughput screening methods to investigate prime editing efficiency

Machine learning approaches to build predictive tools for prime editing

Study A: Predicting prime editing efficiency and product purity by deep learning

Summary

Statement of contribution

Study B: Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning

Summary

Statement of contribution

Discussion

References

Annex I - Manuscript

Annex I - Supplementary Materials

Annex II - Manuscript

Annex II - Supplementary Materials

Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning Dissertation zur Erlangung der naturwissenschaftlichen Doktorwürde ([Dr. sc. nat](Dr. sc. nat).) vorgelegt der Mathematisch-naturwissenschaftlichen Fakultät der Universität Zürich von Nicolas Mathis von Wolfenschiessen NW Promotionskommission: Prof. Dr. Gerald Schwank (Vorsitz, Leitung der Dissertation) Prof. Dr. Martin Jinek Prof. Dr. Karsten Borgwardt Dr. Ulrich Elling Zürich, 2024 Contents Contents ....................................................................................................................................................................... I Summary ................................................................................................................................................................... III Acknowledgments.................................................................................................................................................. V List of Abbreviations .......................................................................................................................................... VII Preface ....................................................................................................................................................................... XI 1. Introduction..................................................................................................................................................... 1 1.1. The rise of genome editing .............................................................................................................. 1 1.2. Cas9 nuclease editing......................................................................................................................... 2 1.3. Base editing ...........................................................................................................................................4 1.4. Prime editing ......................................................................................................................................... 7 1.5. Effect of chromatin on gene editing .......................................................................................... 11 1.6. High-throughput screening methods to investigate prime editing efficiency.......... 11 1.7. Machine learning approaches to build predictive tools for prime editing ................ 16 2. Study A: Predicting prime editing efficiency and product purity by deep learning ........ 19 2.1. Summary.............................................................................................................................................. 20 2.2. Statement of contribution ............................................................................................................. 21 3. Study B: Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning ..................................................................................................................... 23 3.1. Summary.............................................................................................................................................. 24 3.2. Statement of contribution ............................................................................................................. 25 4. Discussion...................................................................................................................................................... 27 References ............................................................................................................................................................... 33 Annex I ..............................................................................................................................................................

FAQs

What is the main goal of this dissertation?

To improve prime editing efficiency by identifying how pegRNA design and local chromatin characteristics influence editing outcomes, supported by machine-learning analyses.

How does prime editing differ from conventional CRISPR-Cas9 editing?

Prime editing can introduce precise edits while minimizing double-strand breaks that are typically associated with conventional CRISPR-Cas9 editing.

What data scale and approach are used to evaluate pegRNA designs?

The work uses high-throughput screening and evaluates more than 92,000 pegRNA designs to uncover key sequence-context determinants of efficient prime editing.

Aug 5, 2026

Upload by Aurora