Abstract
AbstractCRISPR 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.
Funder
Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst
Helmholtz-Zentrum für Infektionsforschung GmbH (HZI)
Publisher
Springer Science and Business Media LLC