ArticleNature communications2019
Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 184 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.
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Who cites it
184 citing papers in PubMed, 1 synthesis or guideline pooled it, 324 citations in OpenAlex.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Machine-learning in optimization of CRISPR technology.Machine learning. Health · 2026Review
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Artificial intelligence and peripheral neuropathies: Strategies for the development, application, and repair of regenerative biomaterials.Neural regeneration research · 2026Article
- Advancements in CRISPR/Cas Technologies for Sensitive Cancer Detection: Mechanisms, Platforms, and Clinical Translation Roadmap.Diagnostics (Basel, Switzerland) · 2026Review
- Chemical and Structural Engineering of Guide RNAs for Precision Genome Editing: From Design Principles to Clinical Applications.Chemical biology & drug design · 2026Review
- Efficient in vivo cytosine base editing using virus-like particles with uracil DNA glycosylase inhibition.Nature biotechnology · 2026Article
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.Bioinformatics (Oxford, England) · 2026Article
- High-throughput evaluation of in vitro CRISPR activities enables optimized large-scale multiplex enrichment of rare variants.Nature biomedical engineering · 2026Article
- Arrayed dual-gRNA CRISPR screening platform forMolecular therapy. Advances · 2026Article
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Review
- Designing genome editing experiments with EditABLE.Genome biology · 2026Article
- 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
- Application of extracellular vesicles in the CRISPR-based diagnosis and treatment: possibilities and challenges.Journal of biological engineering · 2026Review
- Targeted multiplex gene knockouts in Lemna minor using CRISPR/Cas9.Transgenic research · 2026Article
- Immune-evasive stem cells: engineering tolerance and reprogramming microenvironments for regenerative therapy.Stem cell research & therapy · 2026Review
- Optimization of functional genetics tools for a model tetraploid Capsella bursa-pastoris, with focus on homoeolog-aware gene editing.Transgenic research · 2026Article
- Computation and deep-learning-driven advances in CRISPR genome editing.Nature structural & molecular biology · 2026Review
124 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
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA activity for two highly specific SpCas9 variants (eSpCas9(1.1) and SpCas9-HF1) and wild-type SpCas9 (WT-SpCas9) in human cells, and obtain indel rates of over 50,000 gRNAs for each nuclease, covering ~20,000 genes. We evaluate the contribution of 1,031 features to gRNA activity and develope models for activity prediction. Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate that our model outperforms other popular gRNA design tools. Finally, we develop an online design tool DeepHF for the three Cas9 nucleases. The database, as well as the designer tool, is freely accessible via a web server, http://www.DeepHF.com/ .
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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.