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Improved prediction of bacterial CRISPRi guide efficiency through data integration and automated machine learning

Yanying Yu, Sandra Gawlitt, Lisa Barros de Andrade e Sousa, Erinc Merdivan, Marie Piraud, Chase Beisel, View ORCID ProfileLars Barquist
doi: https://doi.org/10.1101/2022.05.27.493707
Yanying Yu
1Helmholtz Institute for RNA-based Infection Research (HIRI) / Helmholtz Centre for Infection Research (HZI), 97080 Würzburg, Germany
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Sandra Gawlitt
1Helmholtz Institute for RNA-based Infection Research (HIRI) / Helmholtz Centre for Infection Research (HZI), 97080 Würzburg, Germany
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Lisa Barros de Andrade e Sousa
2Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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Erinc Merdivan
2Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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Marie Piraud
2Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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Chase Beisel
1Helmholtz Institute for RNA-based Infection Research (HIRI) / Helmholtz Centre for Infection Research (HZI), 97080 Würzburg, Germany
3Medical Faculty, University of Würzburg, 97080 Würzburg, Germany
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Lars Barquist
1Helmholtz Institute for RNA-based Infection Research (HIRI) / Helmholtz Centre for Infection Research (HZI), 97080 Würzburg, Germany
3Medical Faculty, University of Würzburg, 97080 Würzburg, Germany
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  • ORCID record for Lars Barquist
  • For correspondence: lars.barquist@helmholtz-hiri.de
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Abstract

CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is the leading technique to silence gene expression in bacteria. Genome-scale CRISPRi essentiality screens provide one data source from which rules for guide design can be extracted. However, depletion confounds guide efficiency with effects from the targeted gene. Using automated machine learning, we show that depletion can be predicted by a combination of guide and gene features, with expression of the target gene having an outsized influence. Further, integrating data across independent CRISPRi screens improves performance. We develop a mixed-effect random forest regression model that learns from multiple datasets and isolates effects manipulable in guide design, and apply methods from explainable AI to infer interpretable design rules. Our method outperforms the state-of-the-art in predicting depletion in an independent saturating screen targeting purine biosynthesis genes in Escherichia coli. Our approach provides a blueprint for the development of predictive models for CRISPR technologies in bacteria.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 28, 2022.
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Improved prediction of bacterial CRISPRi guide efficiency through data integration and automated machine learning
Yanying Yu, Sandra Gawlitt, Lisa Barros de Andrade e Sousa, Erinc Merdivan, Marie Piraud, Chase Beisel, Lars Barquist
bioRxiv 2022.05.27.493707; doi: https://doi.org/10.1101/2022.05.27.493707
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Improved prediction of bacterial CRISPRi guide efficiency through data integration and automated machine learning
Yanying Yu, Sandra Gawlitt, Lisa Barros de Andrade e Sousa, Erinc Merdivan, Marie Piraud, Chase Beisel, Lars Barquist
bioRxiv 2022.05.27.493707; doi: https://doi.org/10.1101/2022.05.27.493707

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