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-...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Nature biotechnology Ročník 43; číslo 5; s. 712
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.05.2025
Témata:
Animals
Chromatin - genetics
Gene Editing - methods
Humans
Machine Learning
RNA, Guide, CRISPR-Cas Systems - genetics
ISSN:1546-1696, 1546-1696
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1546-1696
1546-1696
DOI:10.1038/s41587-024-02268-2