[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127726-en":3,"doc-seo-127726-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127726,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning - Dissertation","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.","Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning  \nDissertation  \nzur  \nErlangung der naturwissenschaftlichen Doktorwürde  \n([Dr. sc. nat](Dr. sc. nat).)  \nvorgelegt der  \nMathematisch-naturwissenschaftlichen Fakultät  \nder  \nUniversität Zürich  \nvon  \nNicolas Mathis  \nvon  \nWolfenschiessen NW  \nPromotionskommission:  \nProf. Dr. Gerald Schwank (Vorsitz, Leitung der Dissertation) Prof. Dr. Martin Jinek Prof. Dr. Karsten Borgwardt Dr. Ulrich Elling  \nZürich, 2024  \nContents  \nContents ....................................................................................................................................................................... I  \nSummary ................................................................................................................................................................... III  \nAcknowledgments.................................................................................................................................................. V  \nList of Abbreviations .......................................................................................................................................... VII  \nPreface ....................................................................................................................................................................... XI  \n1. Introduction..................................................................................................................................................... 1  \n1.1. The rise of genome editing .............................................................................................................. 1  \n1.2. Cas9 nuclease editing......................................................................................................................... 2  \n1.3. Base editing ...........................................................................................................................................4  \n1.4. Prime editing ......................................................................................................................................... 7  \n1.5. Effect of chromatin on gene editing .......................................................................................... 11  \n1.6. High-throughput screening methods to investigate prime editing efficiency.......... 11  \n1.7. Machine learning approaches to build predictive tools for prime editing ................ 16  \n2. Study A: Predicting prime editing efficiency and product purity by deep learning ........ 19  \n2.1. Summary.............................................................................................................................................. 20  \n2.2. Statement of contribution ............................................................................................................. 21  \n3. Study B: Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning ..................................................................................................................... 23  \n3.1. Summary.............................................................................................................................................. 24  \n3.2. Statement of contribution ............................................................................................................. 25  \n4. Discussion...................................................................................................................................................... 27  \nReferences ............................................................................................................................................................... 33  \nAnnex I ..............................................................................................................................................................","cbCaikvu4js3iyG7","https://ap.wps.com/l/cbCaikvu4js3iyG7","pdf",10069061,2,1,159,"English","en",105,"# Summary\n# Acknowledgments\n# List of Abbreviations\n# Preface\n# Introduction\n## The rise of genome editing\n## Cas9 nuclease editing\n## Base editing\n## Prime editing\n## Effect of chromatin on gene editing\n## High-throughput screening methods to investigate prime editing efficiency\n## Machine learning approaches to build predictive tools for prime editing\n# Study A: Predicting prime editing efficiency and product purity by deep learning\n## Summary\n## Statement of contribution\n# Study B: Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning\n## Summary\n## Statement of contribution\n# Discussion\n# References\n# Annex I - Manuscript\n# Annex I - Supplementary Materials\n# Annex II - Manuscript\n# Annex II - Supplementary Materials","[{\"question\":\"What is the main goal of this dissertation?\",\"answer\":\"To improve prime editing efficiency by identifying how pegRNA design and local chromatin characteristics influence editing outcomes, supported by machine-learning analyses.\"},{\"question\":\"How does prime editing differ from conventional CRISPR-Cas9 editing?\",\"answer\":\"Prime editing can introduce precise edits while minimizing double-strand breaks that are typically associated with conventional CRISPR-Cas9 editing.\"},{\"question\":\"What data scale and approach are used to evaluate pegRNA designs?\",\"answer\":\"The work uses high-throughput screening and evaluates more than 92,000 pegRNA designs to uncover key sequence-context determinants of efficient prime editing.\"}]","Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning - Dissertation | 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