ReviewFrontiers in pharmacology2018
The Current State and Future of CRISPR-Cas9 gRNA Design Tools.
Review in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
53 citing papers in PubMed.
- Trial
- 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
- A rapid Agrobacterium rhizogenes-mediated transient expression for assessing sgRNA efficiency in CRISPR-Act3.0 in tomato.Plant cell reports · 2026Article
- Bacterial mutation dynamics emerging insights into virulence evolution and drug resistance. A review study.Frontiers in medicine · 2026Review
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- Genome editing in maize and sorghum: A comprehensive review of CRISPR/Cas9 and emerging technologies.The plant genome · 2025Review
- The CRISPR-Cas revolution in head and neck cancer: a new era of targeted therapy.Functional & integrative genomics · 2025Review
- Review
- CRISPR-Cas and CRISPR-based screening system for precise gene editing and targeted cancer therapy.Journal of translational medicine · 2024Review
- High-efficiency transgene integration by homology-directed repair in human primary cells using DNA-PKcs inhibition.Nature biotechnology · 2024Article
- Targeted elimination of Vancomycin resistance gene vanA by CRISPR-Cas9 system.BMC microbiology · 2023Article
- Elimination of blaBMC microbiology · 2023Article
- CRISPR-Cas9 System: A Prospective Pathway toward Combatting Antibiotic Resistance.Antibiotics (Basel, Switzerland) · 2023Review
- Targeted mutagenesis with sequence-specific nucleases for accelerated improvement of polyploid crops: Progress, challenges, and prospects.The plant genome · 2023Review
- Multi-faceted CRISPR/Cas technological innovation aspects in the framework of 3P medicine.The EPMA journal · 2023Review
- Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.Briefings in bioinformatics · 2023Review
- Cytosine Deaminase Base Editing to RestoreJID innovations : skin science from molecules to population health · 2023Article
- Shaping the future from the small scale: dry powder inhalation of CRISPR-Cas9 lipid nanoparticles for the treatment of lung diseases.Expert opinion on drug delivery · 2023Review
- Efficient CRISPR/Cas9 nickase-mediated genome editing in an in vitro model of mucopolysaccharidosis IVA.Gene therapy · 2023Article
- Single Molecule FRET Analysis of CRISPR Cas9 Single Guide RNA Folding Dynamics.The journal of physical chemistry. B · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.
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What OpenQuestion holds
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.