ArticleCell discovery2024
Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes.
Article in Cell discovery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Decoding the role of chromatin context in the off-target effects of CRISPR gene editing with EGOLD.Cell discovery · 2026Article
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Deep learning models simultaneously trained on multiple datasets improve base-editing activity prediction.Nature communications · 2025Article
- Base and Prime Editing for Inherited Retinal Diseases: Delivery Platforms, Safety, Efficacy, and Translational Perspectives.Pharmaceutics · 2025Review
- Review
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Graph-CRISPR: a gene editing efficiency prediction model based on graph neural network with integrated sequence and secondary structure feature extraction.Briefings in bioinformatics · 2025Article
- Revolutionizing CRISPR technology with artificial intelligence.Experimental & molecular medicine · 2025Review
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- Precise genome editing process and its applications in plants driven by AI.Functional & integrative genomics · 2025Review
- A comprehensive benchmark for multiple highly efficient base editors with broad targeting scope.bioRxiv : the preprint server for biology · 2025Article
- Systematic empirical evaluation of individual base editing targets: Validating therapeutic targets in USH2A and comparison of methods.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- Integration of Artificial Intelligence and Genome Editing System for Determining the Treatment of Genetic DisordersBalkan medical journal · 2024Article
- Engineering TadA ortholog-derived cytosine base editor without motif preference and adenosine activity limitation.Nature communications · 2024Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.
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Registered trials
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