ArticleNature biotechnology2021
Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning.
Article in Nature biotechnology, 2021. 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 140 papers.
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
140 citing papers in PubMed, 204 citations in OpenAlex.
- Base Editing: Mechanisms and Therapeutic Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- Translating base editors: a decade of progress from the bench to the clinic.Nature reviews. Drug discovery · 2026Review
- Rational selection of SpCas9 and variant-derived adenine base editors for human genome editing.Gene therapy · 2026Article
- A self-iterative orthogonal base-editing platform enables multiplex N-to-N diversification and genome-scale functional screening in Escherichia coli.Nucleic acids research · 2026Article
- Gene Editing-Driven Engineering of Microbial Systems and Metabolites for Precision Medicine and Sustainable Environmental Remediation.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- High-diversity base mutagenesis via simultaneous adenine, cytosine and guanine editing.Nature communications · 2026Article
- Structure-guided engineering of AI-derived adenine base editors for nuclear and mitochondrial DNA editing.Nucleic acids research · 2026Article
- Base Editors for Engineering Industrial Microorganisms: Types, Applications, and Future Perspectives.Biotechnology journal · 2026Review
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Parallel Activation and Interference CRISPR (PAIR) with Sequencing Uncovers DNA Repair Networks Guiding Precision Cell Engineering.bioRxiv : the preprint server for biology · 2026Article
- Indel pattern-guided repair mapping reveals genome-wide DNA repair networks in CRISPR/Cas9 editing.Nucleic acids research · 2026Article
- Rationally and in silico guided APOBEC3F-directed CBE for enhanced PDAC genetic therapy.Communications biology · 2026Article
- Review
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Triple base editor catalyzes saturation mutation of adenine, cytidine, and guanine.Nucleic acids research · 2026Article
- Article
- Article
- Research progress of base editing and prime editing tools based on the CRISPR/Cas system.Molecular therapy. Nucleic acids · 2025Review
- Unlocking mRNA-driven CRISPR-Cas9 gene therapy via optimizing mRNA and the delivery vectors.Molecular therapy. Nucleic acids · 2025Review
- Characterizing and controlling CRISPR repair outcomes in nondividing human cells.Nature communications · 2025Article
80 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
15 authors at 4 institutions in 1 country.
Funding
Abstract
Programmable C•G-to-G•C base editors (CGBEs) have broad scientific and therapeutic potential, but their editing outcomes have proved difficult to predict and their editing efficiency and product purity are often low. We describe a suite of engineered CGBEs paired with machine learning models to enable efficient, high-purity C•G-to-G•C base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect C•G-to-G•C editing outcomes and used these insights to develop CGBEs with diverse editing profiles. We characterized ten promising CGBEs on a library of 10,638 genomically integrated target sites in mammalian cells and trained machine learning models that accurately predict the purity and yield of editing outcomes (R = 0.90) using these data. These CGBEs enable correction to the wild-type coding sequence of 546 disease-related transversion single-nucleotide variants (SNVs) with >90% precision (mean 96%) and up to 70% efficiency (mean 14%). Computational prediction of optimal CGBE-single-guide RNA pairs enables high-purity transversion base editing at over fourfold more target sites than achieved using any single CGBE variant.
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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.