Ultimately, a step-count analysis shows that CasPINS significantly streamlines usability, reducing discrete user actions from 25 to 8 steps compared to the traditional sequential-tool pipeline.
Abstract
Abstract Genome editing researchers currently navigate multiple disconnected tools for guide RNA (gRNA) design, primer generation, and editing analysis—a fragmented workflow that introduces errors and limits reproducibility. CasPINS (Cas-Primer-Indel Suite) addresses this gap as an open-source, unified platform integrating the complete genome editing computational workflow into a single interactive web application accessible without programming expertise. The platform supports 90+ species, 14 CRISPR-Cas variants, TALEN design, and six editing modes. Primer design integrates with Ensembl and NCBI databases relative to cut sites, while indel quantification utilizes Non-Negative Least Squares (NNLS) decomposition of Sanger chromatograms with maximum signal extraction and R2-corrected conservative modes. Benchmarking demonstrates strong concordance with established tools, including a 68.8% recovery of CHOPCHOP gRNAs and 67.2% of CRISPOR gRNAs across five human benchmark genes, alongside an algorithmic agreement within 2.6 percentage points on gold-standard TIDE data. Ultimately, a step-count analysis shows that CasPINS significantly streamlines usability, reducing discrete user actions from 25 to 8 steps compared to the traditional sequential-tool pipeline.
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
The effectiveness of the SaCas9 modular base editors, the robustness of the platform’s modularity, and its feasibility for convenient screening of target-specific base editors are demonstrated.
J. Collantes, Kellen Xu, Melany Ruiz-Urigüen et al.· The CRISPR Journal· 0 citations
Base editors (BEs) enable efficient A-to-G or C-to-T conversions without double-stranded DNA cleavage, but their editing windows remain difficult to tune, limiting genome engineering flexibility. Here, we engineered CRISPR/Cas12b sgRNA by introducing MS2 hairpins to recruit an MS2-N55K-cytidine deaminase-UGI complex, enabling programmable control of the editing window. Three modified sgRNAs were generated by replacing two loop regions, each producing distinct editing hotspots in E. coli. The AID*Δ-MSBE system (sgRNA1.1) generated a window near the PAM with peak activity at C7-C9, while the CDA-MSBE system (sgRNA1.2) produced a distal window with peak activity at C20-C23. Both systems exhibited identical editing patterns in Bacillus subtilis. A dual-orthogonal system (MS2 and PP7) was constructed to simultaneously recruit two deaminase complexes, restoring the classic dCas12b CBE editing pattern. Rifampicin resistance assays confirmed high targeting specificity with low off-target effects. As proof of concept, the MSBEs were successfully employed for the flexible reprogramming of sfGFP fluorescence and the targeted evolution of the endogenous gene rpsE, respectively. Collectively, we developed the MSBEs with tunable editing hotspots, providing innovative tools to enhance the flexibility and accessibility of BEs for genome engineering.
It is argued that AI and CRISPR are complementary components of an emerging design-build-test-learn framework rather than a mature autonomous breeding platform, and progress will depend on plant-specific benchmark datasets, prospective validation, multi-environment field trials, interoperable data standards, equitable access to transformation and computational infrastructure, and governance focused on the properties and evidence of resulting products.
Anilkumar Lalasing Chavan, Pavan Rathod G. P., Chandana Suresh K. S. et al.· Plant cell biotechnology and...· 0 citations
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/
Geun-Woo D. Kim, Dowoon Gu, Mingyo Park et al.· Bioinform.· 0 citations
These strategies avoid the cloud dependency while fragmenting the input, discarding the global context needed to link biological arguments that are distributed across separate papers, and apply the Reduced Interaction Sampling (RIS) engine—a local sparse attention method—to retain the full sequence within the memory envelope of a laboratory server.
A. R. dos Santos· International Journal of Mol...· 0 citations
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