ArticleNucleic acids research2022
Predicting base editing outcomes using position-specific sequence determinants.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- Translating base editors: a decade of progress from the bench to the clinic.Nature reviews. Drug discovery · 2026Review
- Editing around the target: epitope engineering to protect stem cell grafts.Blood advances · 2026Review
- Engineered Transformer Base Editor with Enhanced Editing Efficiency.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deciphering protein mutation-phenotype linkages from CRISPR-based tiling mutagenesis screens.Cell systems · 2026Article
- Synonymous mutations in essential genes infrequently produce fitness effects in human cell lines.Nucleic acids research · 2026Article
- BEstimate: a computational tool for the design and interpretation of CRISPR base editing experiments.Genome biology · 2026Article
- Multiplexed CRISPR base editing enables pulse-activated irreversible biocontainment of engineered bacteria.Nucleic acids research · 2026Article
- High-efficiency TadA cytosine base editors for precise modelling of human disease variants.Nature biomedical engineering · 2026Article
- Feature representation for explainable CRISPR off-target prediction and base editing efficiency.Frontiers in bioinformatics · 2026Article
- Deep learning models simultaneously trained on multiple datasets improve base-editing activity prediction.Nature communications · 2025Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- A streamlined base editor engineering strategy to reduce bystander editing.Nature communications · 2025Article
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A side-by-side comparison of variant function measurements using deep mutational scanning and base editing.Nucleic acids research · 2025Article
- Revolutionizing CRISPR technology with artificial intelligence.Experimental & molecular medicine · 2025Review
- Predicting adenine base editing efficiencies in different cellular contexts by deep learning.Genome biology · 2025Article
- Harnessing novel cytidine deaminases from the animal kingdom for robust multiplexed base editing in rice.Plant biotechnology journal · 2025Article
- Efforts to Downsize Base Editors for Clinical Applications.International journal of molecular sciences · 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
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
CRISPR/Cas base editors promise nucleotide-level control over DNA sequences, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at ∼14 000 target sequences and find that base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, acting to widen or narrow the effective editing window. The impact of features on editing rate depends on the position, with sequence bias efficacy mainly influencing bases away from the center of the window. We use these observations to train a machine learning model to predict editing activity per position, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of disease mutation correcting guides, and find that most of them suffer from more unwanted editing than pure outcomes. This work unravels the position-specificity of base editing biases and allows more efficient planning of editing campaigns in experimental and therapeutic contexts.
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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.