Machine learning prediction of prime editing efficiency across diverse chromatin contexts

The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-...

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Published in:Nature biotechnology Vol. 43; no. 5; p. 712
Main Authors: Mathis, Nicolas, Allam, Ahmed, Tálas, András, Kissling, Lucas, Benvenuto, Elena, Schmidheini, Lukas, Schep, Ruben, Damodharan, Tanav, Balázs, Zsolt, Janjuha, Sharan, Ioannidi, Eleonora I, Böck, Desirée, van Steensel, Bas, Krauthammer, Michael, Schwank, Gerald
Format: Journal Article
Language:English
Published: United States 01.05.2025
Subjects:
Animals
Chromatin - genetics
Gene Editing - methods
Humans
Machine Learning
RNA, Guide, CRISPR-Cas Systems - genetics
ISSN:1546-1696, 1546-1696
Online Access:Get more information
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Summary:The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates.
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ISSN:1546-1696
1546-1696
DOI:10.1038/s41587-024-02268-2