ArticleBMC genomics2022
Comprehensive computational analysis of epigenetic descriptors affecting CRISPR-Cas9 off-target activity.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- 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
- On-target and off-target activities of CRISPR therapeutics across scales.Trends in biotechnology · 2026Review
- Engineering climate-resilient horticultural crops: advances in transcriptional regulation, genome editing, and synthetic networks.Horticulture research · 2026Article
- Decoding the role of chromatin context in the off-target effects of CRISPR gene editing with EGOLD.Cell discovery · 2026Article
- mRNA-engineered CRISPR-Cas epigenetic editors enable durable and efficient gene silencingInnovation (Cambridge (Mass.)) · 2026Article
- Feature representation for explainable CRISPR off-target prediction and base editing efficiency.Frontiers in bioinformatics · 2026Article
- Application of Gene Editing Technology Based on Targeted Delivery Materials in TNBC.ACS omega · 2025Review
- Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR-Cas9 off-target activity.NAR genomics and bioinformatics · 2025Article
- Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification.Cell reports · 2025Article
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Roadmap and Considerations for Genome Editing in a Non-Model Organism: Genetic Variations and Off-Target Profiling.International journal of molecular sciences · 2024Article
- Be-dataHIVE: a base editing database.BMC bioinformatics · 2024Article
- Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs.Nucleic acids research · 2024Article
- Quantifying allele-specific CRISPR editing activity with CRISPECTOR2.0.Nucleic acids research · 2024Article
- Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges.Nucleic acids research · 2024Article
- Designing Epigenome Editors: Considerations of Biochemical and Locus Specificities.Methods in molecular biology (Clifton, N.J.) · 2024Article
- piCRISPR: Physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction.Artificial intelligence in the life sciences · 2023Article
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3 authors.
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Abstract
backgroundA common issue in CRISPR-Cas9 genome editing is off-target activity, which prevents the widespread use of CRISPR-Cas9 in medical applications. Among other factors, primary chromatin structure and epigenetics may influence off-target activity.
methodsIn this work, we utilize crisprSQL, an off-target database, to analyze the effect of 19 epigenetic descriptors on CRISPR-Cas9 off-target activity. Termed as 19 epigenetic features/scores, they consist of 6 experimental epigenetic and 13 computed nucleosome organization-related features. In terms of novel features, 15 of the epigenetic scores are newly considered. The 15 newly considered scores consist of 13 freshly computed nucleosome occupancy/positioning scores and 2 experimental features (MNase and DRIP). The other 4 existing scores are experimental features (CTCF, DNase I, H3K4me3, RRBS) commonly used in deep learning models for off-target activity prediction. For data curation, MNase was aggregated from existing experimental nucleosome occupancy data. Based on the sequence context information available in crisprSQL, we also computed nucleosome occupancy/positioning scores for off-target sites.
resultsTo investigate the relationship between the 19 epigenetic features and off-target activity, we first conducted Spearman and Pearson correlation analysis. Such analysis shows that some computed scores derived from training-based models and training-free algorithms outperform all experimental epigenetic features. Next, we evaluated the contribution of all epigenetic features in two successful machine/deep learning models which predict off-target activity. We found that some computed scores, unlike all 6 experimental features, significantly contribute to the predictions of both models. As a practical research contribution, we make the off-target dataset containing all 19 epigenetic features available to the research community.
conclusionsOur comprehensive computational analysis helps the CRISPR-Cas9 community better understand the relationship between epigenetic features and CRISPR-Cas9 off-target activity.
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