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

Prime editing is a powerful genome editing technology, but its efficiency varies depending on the pegRNA design and target locus. Existing computational models for predicting prime editing rates are limited by their focus on specific edit types and by omitting the local chromatin environment. In our...

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Veröffentlicht in:bioRxiv
Hauptverfasser: Mathis, Nicolas, Allam, Ahmed, Tálas, András, Benvenuto, Elena, Schep, Ruben, Damodharan, Tanav, Balázs, Zsolt, Janjuha, Sharan, Schmidheini, Lukas, Böck, Desirée, van Steensel, Bas, Krauthammer, Michael, Schwank, Gerald
Format: Paper
Sprache:Englisch
Veröffentlicht: Cold Spring Harbor Laboratory 09.10.2023
Ausgabe:1.1
Schlagworte:
Molecular Biology
ISSN:2692-8205
Online-Zugang:Volltext
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Zusammenfassung:Prime editing is a powerful genome editing technology, but its efficiency varies depending on the pegRNA design and target locus. Existing computational models for predicting prime editing rates are limited by their focus on specific edit types and by omitting the local chromatin environment. In our study, we developed machine learning models that predict prime editing efficiencies across a wide range of edit types up to 15 bp (’PRIDICT2.0’) and in different chromatin contexts (’ePRIDICT’). Both models can be accessed at www.pridict.it.
Bibliographie:Competing Interest Statement: G.S. is a scientific advisor to Prime Medicine.
ISSN:2692-8205
DOI:10.1101/2023.10.09.561414