ArticleBioinformatics (Oxford, England)2021
CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- CROP: a feature-independent context-aware method for CRISPR-Cas9 frameshift prediction.Bioinformatics (Oxford, England) · 2026Article
- CRISPR/Cas9-Based Genome Editing: Understanding Differences in DNA Repair Pathways, Profiles, and Outcomes.International journal of molecular sciences · 2026Review
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- Optimizing CRISPR precision in mouse embryos via microhomology-mediated end joining-dominant targeting.Communications biology · 2026Article
- CasMiner: a deep-learning tool for high-throughput mining and rational design of efficient Cas9.National science review · 2026Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- Establishment of a CRISPR/Cas9 gene editing system based on growth points transformation method in Fraxinus mandshurica.BMC plant biology · 2025Article
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A versatile CRISPR/Cas9 system off-target prediction tool using language model.Communications biology · 2025Article
- X-CRISP: Domain-Adaptable and Interpretable CRISPR Repair Outcome Prediction.bioRxiv : the preprint server for biology · 2025Article
- 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
- X-CRISP: domain-adaptable and interpretable CRISPR repair outcome prediction.Bioinformatics advances · 2025Article
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- A tool for CRISPR-Cas9 sgRNA evaluation based on computational models of gene expression.Genome medicine · 2024Article
- DeepIndel: An Interpretable Deep Learning Approach for Predicting CRISPR/Cas9-Mediated Editing Outcomes.International journal of molecular sciences · 2024Article
- Codon usage and expression-based features significantly improve prediction of CRISPR efficiency.NPJ systems biology and applications · 2024Article
- Review
- Strong association between genomic 3D structure and CRISPR cleavage efficiency.PLoS computational biology · 2024Article
- gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement.Biomolecules · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationCRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 editing outcomes depend on local DNA sequences at the target site and are thus predictable. However, existing prediction methods are dependent on both feature and model engineering, which restricts their performance to existing knowledge about CRISPR/Cas9 editing.
resultsHerein, deep multi-task convolutional neural networks (CNNs) and neural architecture search (NAS) were used to automate both feature and model engineering and create an end-to-end deep-learning framework, CROTON (CRISPR Outcomes Through cONvolutional neural networks). The CROTON model architecture was tuned automatically with NAS on a synthetic large-scale construct-based dataset and then tested on an independent primary T cell genomic editing dataset. CROTON outperformed existing expert-designed models and non-NAS CNNs in predicting 1 base pair insertion and deletion probability as well as deletion and frameshift frequency. Interpretation of CROTON revealed local sequence determinants for diverse editing outcomes. Finally, CROTON was utilized to assess how single nucleotide variants (SNVs) affect the genome editing outcomes of four clinically relevant target genes: the viral receptors ACE2 and CCR5 and the immune checkpoint inhibitors CTLA4 and PDCD1. Large SNV-induced differences in CROTON predictions in these target genes suggest that SNVs should be taken into consideration when designing widely applicable gRNAs. AVAILABILITY AND IMPLEMENTATION: https://github.com/vli31/CROTON. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.