ArticleNature communications2021
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.
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 113 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
113 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- The global patent landscape of CRISPR-Cas applications in medicine.Nature biotechnology · 2026Article
- Auto-SMART: a web-based platform for repair-template design for CRISPR/Cas9 homology-directed editing.bioRxiv : the preprint server for biology · 2026Article
- The landscape and trajectory of global CRISPR therapeutics.Molecular therapy. Nucleic acids · 2026Review
- High-Content CRISPR Screening: Methods and Applications.MedComm · 2026Review
- Crosstalk between S-nitrosylation and glycation defines a metabolic vulnerability in liver and renal cancers.Nature communications · 2026Article
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- High-throughput evaluation of in vitro CRISPR activities enables optimized large-scale multiplex enrichment of rare variants.Nature biomedical engineering · 2026Article
- CRISPR Gene Tagging for Illuminating Endogenous Protein Dynamics.International journal of molecular sciences · 2026Review
- The evolution of AI-integrated genome editing and its challenges.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Balancing off-target and on-target considerations for optimized CRISPR-Cas9 knockout library design.Cell genomics · 2026Article
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- Optimizing CRISPR precision in mouse embryos via microhomology-mediated end joining-dominant targeting.Communications biology · 2026Article
- Engineering next-generation crops through CRISPR-mediated horizontal gene transfer.The New phytologist · 2026Review
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Features affecting Cas9-induced editing efficiency and patterns in tomato: evidence from a large CRISPR dataset.The Plant journal : for cell and molecular biology · 2026Article
- Computation and deep-learning-driven advances in CRISPR genome editing.Nature structural & molecular biology · 2026Review
- CRISPR-on-Chip for Point-of-Care Diagnostics.ACS nano · 2026Review
53 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/ . CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools.
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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.