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Series GSE196911 Query DataSets for GSE196911
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
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.
 
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.
 
Contributor(s) Yu Y, Gawlitt S, Beisel C, Barquist L
Citation(s) 38200565
Submission date Feb 16, 2022
Last update date Jan 18, 2024
Contact name Yanying Yu
E-mail(s) yanying.yu@helmholtz-hiri.de
Organization name Helmholtz Institute for RNA-based Infection Research
Lab INTEGRATIVE INFORMATICS FOR INFECTION BIOLOGY
Street address Josef-Schneider-Str. 2 / D15
City Würzburg
State/province DE Deutschland
ZIP/Postal code 97080
Country Germany
 
Platforms (1)
GPL25368 Illumina NovaSeq 6000 (Escherichia coli)
Samples (8)
GSM5904624 Input_1
GSM5904625 Input_2
GSM5904626 OD02_1
Relations
BioProject PRJNA807710

Download family Format
SOFT formatted family file(s) SOFTHelp
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Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE196911_purine_screen_logFC.csv.gz 41.9 Kb (ftp)(http) CSV
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