ArticleNucleic acids research2024
Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- SpacerScope: binary-vectorized, genome-wide off-target profiling for RNA-guided nucleases without prior candidate-site bias.Briefings in bioinformatics · 2026Article
- Development of Knockout Cardiac Muscle Cell Lines Using Integrase-Deficient Lentivirus-Mediated CRISPR/Cas9 Gene Editing.Biochemical genetics · 2026Article
- CROP: a feature-independent context-aware method for CRISPR-Cas9 frameshift prediction.Bioinformatics (Oxford, England) · 2026Article
- Guide RNA reprogramming facilitates minimized tracrRNA-dependent off-target and versatile CRISPR/Cas9 engineering.Nature communications · 2026Article
- Balancing off-target and on-target considerations for optimized CRISPR-Cas9 knockout library design.Cell genomics · 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
- DNA and RNA editing for the therapy of human diseases: current status, challenges, and future prospects.Molecular biomedicine · 2026Review
- Feature representation for explainable CRISPR off-target prediction and base editing efficiency.Frontiers in bioinformatics · 2026Article
- Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction.PLoS computational biology · 2025Article
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
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- Off-target sequence variations driven by the intrinsic properties of the Cas-sgRNA-DNA complex in genome editing.PloS one · 2025Article
- Crispr-SGRU: Prediction of CRISPR/Cas9 Off-Target Activities with Mismatches and Indels Using Stacked BiGRU.International journal of molecular sciences · 2024Article
- DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 on-target editing efficiency in specific cellular contexts.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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2 authors.
Funding
Abstract
The CRISPR/Cas9 system is a highly accurate gene-editing technique, but it can also lead to unintended off-target sites (OTS). Consequently, many high-throughput assays have been developed to measure OTS in a genome-wide manner, and their data was used to train machine-learning models to predict OTS. However, these models are inaccurate when considering OTS with bulges due to limited data compared to OTS without bulges. Recently, CHANGE-seq, a new in vitro technique to detect OTS, was used to produce a dataset of unprecedented scale and quality. In addition, the same study produced in cellula GUIDE-seq experiments, but none of these GUIDE-seq experiments included bulges. Here, we generated the most comprehensive GUIDE-seq dataset with bulges, and trained and evaluated state-of-the-art machine-learning models that consider OTS with bulges. We first reprocessed the publicly available experimental raw data of the CHANGE-seq study to generate 20 new GUIDE-seq experiments, and hundreds of OTS with bulges among the original and new GUIDE-seq experiments. We then trained multiple machine-learning models, and demonstrated their state-of-the-art performance both in vitro and in cellula over all OTS and when focusing on OTS with bulges. Last, we visualized the key features learned by our models on OTS with bulges in a unique representation.
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