ArticleNucleic acids research2022
CRISPR-Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
122 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
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- 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
- Minimizing Off-Target Effects of CRISPR-Cas9 With Optimized sgRNA: Evaluation of Efficiency and Specificity in the Tumor Protein 53 (TP53) Region.Biotechnology and bioengineering · 2026Article
- Technical limitations of CRISPR-Cas9 genome editing in bacteria: challenges and future directions.Archives of microbiology · 2026Review
- AI-Assisted Cross-Study Synthesis in Genome Editing: Comparing Long-Context Strategies and Uncovering Latent Contradictions in the CRISPR-Cas9 Guide RNA Prediction Literature.International journal of molecular sciences · 2026Article
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- 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
- 10 years of CRISPR/CAS genomic engineering in Yarrowia lipolytica.Bioprocess and biosystems engineering · 2026Review
- Adenine base editor for knockout of proteins: A practical guide from design to analysis with updated MultiEditRbatch.Molecular therapy. Nucleic acids · 2026Article
- Arrayed dual-gRNA CRISPR screening platform forMolecular therapy. Advances · 2026Article
- Efficient genome editing using CRISPR-Cas9 in reef-building corals.Nature protocols · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Therapeutic Applications of Natural Products in Biomedicine and Pharmacotherapy.Life (Basel, Switzerland) · 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
- Evaluation of computational tools for the prediction of CRISPR/SpCas9 gRNA activity in plants.Plant cell reports · 2026Article
- PALINCODE: Recording cell lineage with ternary palindromic CRISPR bits.bioRxiv : the preprint server for biology · 2026Article
- Targeted multiplex gene knockouts in Lemna minor using CRISPR/Cas9.Transgenic research · 2026Article
62 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR-Cas9 guide design.
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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.