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Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration.
Genome Biol. 25:13 (2024)
CRISPR interference (CRISPRi) is the leading technique to silence gene expression in bacteria; however, design rules remain poorly defined. We develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in genome-wide essentiality screens, with the surprising discovery that gene-specific features substantially impact prediction. We develop a mixed-effect random forest regression model that provides better estimates of guide efficiency. We further apply methods from explainable AI to extract interpretable design rules from the model. This study provides a blueprint for predictive models for CRISPR technologies where only indirect measurements of guide activity are available.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Gene; Design; Sgrnas
ISSN (print) / ISBN
1474-760X
e-ISSN
1465-6906
Journal
Genome Biology
Quellenangaben
Volume: 25,
Issue: 1,
Article Number: 13
Publisher
BioMed Central
Publishing Place
Campus, 4 Crinan St, London N1 9xw, England
Non-patent literature
Publications
Reviewing status
Peer reviewed
Grants
Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst