ArticleCommunications biology2025
A versatile CRISPR/Cas9 system off-target prediction tool using language model.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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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
13 citing papers in PubMed.
- Development of Knockout Cardiac Muscle Cell Lines Using Integrase-Deficient Lentivirus-Mediated CRISPR/Cas9 Gene Editing.Biochemical genetics · 2026Article
- Emerging strategies to reduce the side effects of CAR-T cell therapy: focusing on gene editing and nanotechnology.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Engineering the Gut Microbiome: Emerging Genome-Editing Strategies and Therapeutic Applications.Microorganisms · 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
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Review
- In Silico Design and Characterization of a Rationally Engineered Cas12j2 Gene Editing System for the Treatment of HPV-Associated Cancers.International journal of molecular sciences · 2026Article
- Spatiotemporal regulation of acute wound healing by the NLRP3 inflammasome: dual roles in macrophage-fibroblast chemotaxis and phenotype during wound repair.Burns & trauma · 2026Article
- Engineering a High-Fidelity MAD7 Variant with Enhanced Specificity for Precision Genome Editing via CcdB-Based Bacterial Screening.Biomolecules · 2025Article
- A versatile CRISPR/Cas9 system off-target prediction tool using language model.Communications biology · 2025Article
- Engineering with care: safety assessment platforms for CRISPR-modified natural killer cells.Frontiers in immunology · 2025Review
- Advances in WRKY regulation of immune responses in medicinal plants.Frontiers in plant science · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Genome editing with the CRISPR/Cas9 system has revolutionized life and medical sciences, particularly in treating monogenic genetic diseases by enabling long-term therapeutic effects from a single intervention. However, the CRISPR/Cas9 system can tolerate mismatches and DNA/RNA bulges at target sites, leading to unintended off-target effects that pose challenges for gene-editing therapy development. Existing high-throughput detection and in silico prediction methods are often limited to specifically designed single guide RNAs (sgRNAs) and perform poorly on unseen sequences. To address these limitations, we introduce CCLMoff, a deep learning framework for off-target prediction that incorporates a pretrained RNA language model from RNAcentral. CCLMoff captures mutual sequence information between sgRNAs and target sites and is trained on a comprehensive, updated dataset. This approach enables accurate off-target identification and strong generalization across diverse NGS-based detection datasets. Model interpretation reveals the biological importance of the seed region, underscoring CCLMoff's analytical capabilities. The development of CCLMoff lays the foundation for a comprehensive, end-to-end sgRNA design platform, enhancing both the precision and efficiency of CRISPR/Cas9-based therapeutics. CCLMoff is a versatile tool and is publicly available at github.com/duwa2/CCLMoff .
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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.