ArticleNature communications2021
Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Structure-aware graph learning predicts RNA editability across tissues and species.Research square · 2026Article
- Improved RNA base editing with guide RNAs mimicking highly edited endogenous ADAR substrates.Nature biotechnology · 2026Article
- Multiplexed assays of variant effect for clinical variant interpretation.Nature reviews. Genetics · 2026Review
- ADAR-GPT: A continually fine-tuned language model for predicting A-to-I RNA editing sites.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- An engineered U7 small nuclear RNA scaffold greatly increases ADAR-mediated programmable RNA base editing.Nature communications · 2025Article
- Biochemical profiling and structural basis of ADAR1-mediated RNA editing.Molecular cell · 2025Article
- Precise in vivo RNA base editing with a wobble-enhanced circular CLUSTER guide RNA.Nature biotechnology · 2025Article
- DEMINING: A deep learning model embedded framework to distinguish RNA editing from DNA mutations in RNA sequencing data.Genome biology · 2024Article
- Precision RNA base editing with engineered and endogenous effectors.Nature biotechnology · 2023Review
- Machine learning-aided scoring of synthesis difficulties for designer chromosomes.Science China. Life sciences · 2023Article
- RNA editing: Expanding the potential of RNA therapeutics.Molecular therapy : the journal of the American Society of Gene Therapy · 2023Review
- UncoveringmBio · 2022Article
- A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.Computational and structural biotechnology journal · 2022Review
- Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis.Nature communications · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code.
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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.