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Status |
Public on Dec 19, 2023 |
Title |
Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration |
Organism |
Escherichia coli |
Experiment type |
Other
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Summary |
CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is the leading technique to silence gene expression in bacteria. However, design rules for CRISPRi remain poorly defined, limiting predictable design for gene interrogation, pathway manipulation, and high-throughput screens. Here we develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in multiple genome-wide essentiality screens, with the surprising discovery that gene-specific features such as transcriptional activity substantially impact prediction of guide activity. Accounting for these features as part of algorithm development allowed us to develop a mixed-effect random forest regression model that provides better estimates of guide efficiency than existing methods, as demonstrated in an independent saturating screen. We further applied methods from explainable AI to extract interpretable design rules from the model, such as sequence preferences in the vicinity of the PAM distinct from those previously described for genome engineering applications. Our approach provides a blueprint for the development of predictive models for CRISPR technologies where only indirect measurements of guide activity are available.
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Overall design |
750 gRNAs were designed to target 9 purine biosynthesis genes, with between 35 and 233 guides per gene. Duplicate samples were then collected at three time points during growth in M9 minimal media, and gRNA depletion was measured with reference to input samples, normalized using a set of 50 gRNAs designed not to target any E. coli sequence.
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Contributor(s) |
Yu Y, Gawlitt S, Beisel C, Barquist L |
Citation(s) |
38200565 |
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Submission date |
Feb 16, 2022 |
Last update date |
Jan 18, 2024 |
Contact name |
Yanying Yu |
E-mail(s) |
yanying.yu@helmholtz-hiri.de
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Organization name |
Helmholtz Institute for RNA-based Infection Research
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Lab |
INTEGRATIVE INFORMATICS FOR INFECTION BIOLOGY
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Street address |
Josef-Schneider-Str. 2 / D15
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City |
Würzburg |
State/province |
DE Deutschland |
ZIP/Postal code |
97080 |
Country |
Germany |
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Platforms (1) |
GPL25368 |
Illumina NovaSeq 6000 (Escherichia coli) |
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Samples (8)
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Relations |
BioProject |
PRJNA807710 |