Abstract Summary CRISPR-Cas9 has become a widely used tool for genome editing. However, its off-target cleavage caused by partial sequence matches with guide RNAs (gRNAs) remains a critical limitation. Recently, abasic gRNAs (ØXØ) have been developed to enhance target specificity, but their effects vary depending on the positional sequence context. Here, we present abCRISPR, a deep neural network (DNN) framework for the rational design of ØXØ sequences with minimized off-target activity. abCRISPR leverages informative few-shot training with paired datasets of abasic and unmodified gRNAs, using high-quality random mismatch target libraries, exhaustively sequenced for mismatched off-target substrates (n = 97583) in in vitro CRISPR-Cas9 cleavage experiments. Predicted off-target activities for both abasic and unmodified gRNAs showed strong correlation with experimental data (r ≥ 0.95, 10-fold cross-validation). Notably, these comprehensive training sets provide robust ground-truth negatives, enabling accurate and sensitive prediction of off-targets. For unmodified gRNAs, abCRISPR (AUC = 0.98) was validated to outperform existing deep learning-based methods (AUC = 0.45–0.68). When applied to the human genome, abCRISPR generated ØXØ sequences, covering 58 875 004 potent CRISPR-targetable sites with improved target specificity. Together, this work provides a comprehensive bioinformatics resource for safe and precise CRISPR-Cas9 genome editing. Availability and implementation The source code for abCRISPR and training data are available at https://doi.org/10.5281/zenodo.20398246. abCRISPR results for the human genome are available at http://clip.korea.ac.kr/abCRISPR/
Five machine learning classifiers are benchmarked on a real, published GUIDE-seq off-target dataset and the low absolute precision achievable in this severely imbalanced, small-positive-class setting is reported, as a realistic picture of what off-target classifiers can and cannot yet deliver from sequence alone.
Accurate identification of CRISPR-Cas9 off-target sites is essential for the safety assessment of genome-editing-based therapies. While numerous in silico prediction tools have been developed, their comparative performance and practical utility in preclinical workflows remain incompletely defined. We performed a systematic benchmarking of 14 in silico CRISPR-Cas9 off-target prediction tools, including both standard approaches and machine learning-based models. The analysis was based on a curated dataset derived from the CRISPRoffT database, comprising 3,827 deep-sequenced genomic sites across 26 guide RNA/Cas9 combinations in human cells. Sites with indel frequencies ≥0.1% were operationally defined as true off-targets. We evaluated tool performance using score distributions, correlation with indel frequencies, precision-recall characteristics, recall among top-ranked candidate sites, and the effect of combining tools. All tools assigned higher scores to true off-target sites compared with nontarget sites, although substantial overlap between classes was observed. Correlation between prediction scores and indel frequencies was weak to moderate, indicating limited ability to predict editing magnitude. Precision-recall performance was moderate across all tools, reflecting inherent trade-offs between sensitivity and specificity. Recall increased with the number of predicted sites considered, reaching approximately 77% among the top 500 and up to 83% among the top 1,250 sites, but leaving a substantial fraction of true off-targets undetected. Combining tools yielded only modest improvements. Current in silico tools enable prioritization of CRISPR-Cas9 off-target candidates but remain limited in their ability to comprehensively identify and quantitatively predict off-target activity. Our findings highlight the importance of considering both ranking performance and candidate site coverage and support the use of combined computational and experimental strategies for robust off-target assessment in preclinical gene editing workflows.
M. M. Kaufmann, Maren Hackenberg, William Jobson Pargeter et al.· Human Gene Therapy· 0 citations
UCRISPRa achieved selective activation of olfr544 among more than a thousand homologous olfactory receptor genes in skeletal muscle cells, leading to enhanced mitochondrial biogenesis and broad potential for precise gene regulation and functional studies of complex macromolecular systems.
Smith Le, Trung Thach· The FEBS Journal· 0 citations
DeepCRISPR-Typer is presented, a comprehensive computational framework integrating a large protein language model (TEMC-Cas), a deep sequence feature extractor (CRISPR-RepTyper), and an adaptive targeted HMM profiling strategy that significantly reduces computational overhead by dynamically invoking subtype-specific HMM subsets.
Long Wen, Minghui Jing, Yanyan Li et al.· ACS Synthetic Biology· 0 citations
This work compares the performance of multiple KRAB domain systems, develops an updated CRISPRi-specific on-target scoring scheme, and quantitatively characterize off-target effects associated with seed-sequence patterns.
Smriti Srikanth, Fengyi Zheng, Laura M Drepanos et al.· Cell Genomics· 0 citations
Traditional CRISPR-Cas12a mutation detection systems are limited by poor single-base specificity, target-specific crRNA redesign, and insufficient sensitivity for low-abundance mutations, restricting their clinical liquid biopsy applications. Herein, we developed a crRNA-universal, sensitive and specific CRISPR-Cas12a detection platform, termed DESIC (double-end blocker and split-input mediated CRISPR-Cas12a system), for single-base mutation detection. The DESIC system adopts two key structural designs: double-end blocker (DEB) and duplicated split-input (SIN). The DEB spatially isolates crRNA recognition and target-binding regions, enabling universal detection of various mutation sites without crRNA redesign. The SIN strategy amplifies thermodynamic differences from single-base mismatches, greatly improving single-nucleotide discrimination. We targeted four prevalent pancreatic cancer KRAS mutations (G12D, G12R, G12V, Q61H) and optimized the system to achieve optimal discrimination. The optimized DESIC system exhibited ultra-low limits of detection down to 0.01% mutant allele fraction with reliable linear quantitative performance. Clinical validation using 15 pairs of pancreatic cancer tissue and peripheral blood samples confirmed that DESIC results were highly consistent with gold-standard NGS data. With a flexible modular design, this low-cost, easy-operated platform can be readily extended to multiple tumor mutations, holding great potential for tumor liquid biopsy and early molecular diagnosis.
Shi-Zhen Li, Yangwei Liao, Xiaoxiang Wang et al.· Biosensors & bioelectronics· 0 citations
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