ArticleGenome biology2024
Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Multi-layered metabolic remodeling ofMetabolic engineering communications · 2026Article
- AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.Journal of translational medicine · 2026Review
- Gene circuit-based sensors.Fundamental research · 2025Review
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- A CRISPRi library screen in group BInfection and immunity · 2025Article
- ASOBIOTICS 2024: an interdisciplinary symposium on antisense-based programmable RNA antibiotics.RNA (New York, N.Y.) · 2025Article
- Engineering Useful Microbial Species for Pharmaceutical Applications.Microorganisms · 2025Review
- GLiDe: a web-based genome-scale CRISPRi sgRNA design tool for prokaryotes.BMC bioinformatics · 2025Article
- Programming CRISPRi to control the lifecycle of bacteriophage T7.Frontiers in microbiology · 2025Article
- Machine learning in infectious diseases: potential applications and limitations.Annals of medicine · 2024Review
- The rise and future of CRISPR-based approaches for high-throughput genomics.FEMS microbiology reviews · 2024Review
- Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling.Nature communications · 2024Article
- Systematic interrogation of CRISPR antimicrobials in Klebsiella pneumoniae reveals nuclease-, guide- and strain-dependent features influencing antimicrobial activity.Nucleic acids research · 2024Article
- Expanding the flexibility of base editing for high-throughput genetic screens in bacteria.Nucleic acids research · 2024Article
- Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration.Genome biology · 2024Article
- Application of functional genomics for domestication of novel non-model microbes.Journal of industrial microbiology & biotechnology · 2024Review
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.