ArticleNature communications2022
Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 51 citations in OpenAlex.
- Yeast species resource exploration and application in cell factories.Synthetic and systems biotechnology · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- 10 years of CRISPR/CAS genomic engineering in Yarrowia lipolytica.Bioprocess and biosystems engineering · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- Revolutionizing CRISPR technology with artificial intelligence.Experimental & molecular medicine · 2025Review
- Highly parallel profiling of the activities and specificities of Cas12a variants in human cells.Nature communications · 2025Article
- Advances in CRISPR-enabled genome-wide screens in yeast.FEMS yeast research · 2025Review
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Optimizing multicopy chromosomal integration for stable high-performing strains.Nature chemical biology · 2024Article
- Balanced Training Sets Improve Deep Learning-Based Prediction of CRISPR sgRNA Activity.ACS synthetic biology · 2024Article
- Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering.Journal of agricultural and food chemistry · 2024Review
- CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration.Nucleic acids research · 2024Article
- Review
- Comparison of CRISPR-MAD7 and CRISPR-Cas9 for Gene Disruptions inJournal of fungi (Basel, Switzerland) · 2024Article
- High-Efficiency Multiplexed Cytosine Base Editors for Natural Product Synthesis inACS synthetic biology · 2023Article
- Characterization of an efficient CRISPR-iCas9 system in Yarrowia lipolytica for the biosynthesis of carotenoids.Applied microbiology and biotechnology · 2023Article
- A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets.Nature communications · 2023Article
- A DNA assembly toolkit to unlock the CRISPR/Cas9 potential for metabolic engineering.Communications biology · 2023Article
- acCRISPR: an activity-correction method for improving the accuracy of CRISPR screens.Communications biology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide functional genetic screens have been successful in discovering genotype-phenotype relationships and in engineering new phenotypes. While broadly applied in mammalian cell lines and in E. coli, use in non-conventional microorganisms has been limited, in part, due to the inability to accurately design high activity CRISPR guides in such species. Here, we develop an experimental-computational approach to sgRNA design that is specific to an organism of choice, in this case the oleaginous yeast Yarrowia lipolytica. A negative selection screen in the absence of non-homologous end-joining, the dominant DNA repair mechanism, was used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a. This genome-wide data served as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuide's ability to accurately predict high activity sgRNAs. DeepGuide provides an organism specific predictor of CRISPR guide activity that with retraining could be applied to other fungal species, prokaryotes, and other non-conventional organisms.
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