ArticleNature biotechnology2025
Machine learning prediction of prime editing efficiency across diverse chromatin contexts.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 49 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Machine-learning in optimization of CRISPR technology.Machine learning. Health · 2026Review
- Advances in prime editing: Molecular innovations, Large-fragment engineering, and AI-driven design.Biodesign research · 2026Review
- Article
- Mechanistic machine learning for prediction of prime editing outcomes.Nature biotechnology · 2026Article
- Delivering the future of immunotherapy: A state-of-the-art review of gene editing in immune cells with lipid nanoparticles.Materials today. Bio · 2026Review
- Epigenetic remodeling during UV exposure: high resolution analysis of histone post-translational modifications in a DNA binding protein 2 mutant model.Histochemistry and cell biology · 2026Article
- Protocol for generating multiplexed prime-edited monoclonal cell lines in porcine fetal fibroblasts.STAR protocols · 2026Article
- Prime editing in neuropsychiatric disorders: From mutation-specific target selection to clinical translation.Neuroprotection (Chichester, England) · 2026Review
- Gigabase-scale deletion scanning of the human genome.bioRxiv : the preprint server for biology · 2026Article
- Prime editing updates: technological evolution, methodological expansion, and delivery strategies for in vivo applications.BMB reports · 2026Review
- Directed evolution of small RNA-stabilizing motifs that improve prime-editing efficiency.Nature biotechnology · 2026Article
- RNA-LNP-mediated in vivo prime editing corrects disease phenotypes in a mouse model of citrullinemia type I.Science translational medicine · 2026Article
- Prime editing of a pathogenicScience translational medicine · 2026Article
- A multiplex, prime editing framework for identifying drug resistance variants at scale.Cell genomics · 2026Article
- Designing genome editing experiments with EditABLE.Genome biology · 2026Article
- Disrupting pegRNA intramolecular complementarity via PBS and spacer sequence alterations can enhance prime editing efficiency.Nucleic acids research · 2026Article
- High-precision cytosine base editors by evolving nucleic-acid-recognition hotspots in deaminase.Nature biotechnology · 2026Article
- Systematic pegRNA design with PRIDICT2.0 and ePRIDICT for efficient prime editing.Nature protocols · 2026Review
- Evolution of Prime Editing: Enhancing Efficiency and Expanding Capacity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
Abstract
The success of prime editing depends on the prime editing guide RNA (pegRNA) design and target locus. Here, we developed machine learning models that reliably predict prime editing efficiency. PRIDICT2.0 assesses the performance of pegRNAs for all edit types up to 15 bp in length in mismatch repair-deficient and mismatch repair-proficient cell lines and in vivo in primary cells. With ePRIDICT, we further developed a model that quantifies how local chromatin environments impact prime editing rates.
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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.