The rapid development of gene editing technology has revolutionized life science research and biotechnology applications. Prime editing, a precise gene editing tool, has shown promise in various applications, including disease research and therapeutic interventions. However, its suboptimal editing efficiency for extensive fragments and lack of predictive models have hindered its widespread adoption. Existing models exhibit low prediction accuracy and limitations, such as neglecting epigenetic factors that impact gene editing effects. To address these challenges, we developed PrimeNet, a novel prediction model that integrates significant epigenetic factors, including chromatin accessibility and DNA methylation. By incorporating data from multiple cell lines and introducing multiscale convolution and attention mechanisms, PrimeNet enhances the accuracy of predictions and generalization performance. Our results show that PrimeNet achieves a Spearman correlation coefficient of 0.94 and 0.82 on two datasets originated from HEK293T and K562 cell lines, respectively, outperforming existing models. This novel model has the potential to guide experimental design, enhance the success rate of gene editing, and reduce unnecessary experimental costs, thereby advancing the application of gene editing technology in genetic disease treatment and related fields.

MOTIVATION: Quantitative structure-activity relationship (QSAR) analysis is commonly used in drug discovery. Collaborations among pharmaceutical institutions can lead to a better performance in QSAR prediction, however, intellectual property and related financial interests remain substantially hindering inter-institutional collaborations in QSAR modeling for drug discovery. RESULTS: For the first time, we verified the feasibility of applying the horizontal federated learning (HFL), which is a recently developed collaborative and privacy-preserving learning framework to perform QSAR analysis. A prototype platform of federated-learning-based QSAR modeling for collaborative drug discovery, i.e. FL-QSAR, is presented accordingly. We first compared the HFL framework with a classic privacy-preserving computation framework, i.e. secure multiparty computation to indicate its difference from various perspective. Then we compared FL-QSAR with the public collaboration in terms of QSAR modeling. Our extensive experiments demonstrated that (i) collaboration by FL-QSAR outperforms a single client using only its private data, and (ii) collaboration by FL-QSAR achieves almost the same performance as that of collaboration via cleartext learning algorithms using all shared information. Taking together, our results indicate that FL-QSAR under the HFL framework provides an efficient solution to break the barriers between pharmaceutical institutions in QSAR modeling, therefore promote the development of collaborative and privacy-preserving drug discovery with extendable ability to other privacy-related biomedical areas. AVAILABILITY AND IMPLEMENTATION: The source codes of FL-QSAR are available on the GitHub: https://github.com/bm2-lab/FL-QSAR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

For genome-wide CRISPR off-target cleavage sites (OTS) prediction, an important issue is data imbalance-the number of true OTS recognized by whole-genome off-target detection techniques is much smaller than that of all possible nucleotide mismatch loci, making the training of machine learning model very challenging. Therefore, computational models proposed for OTS prediction and scoring should be carefully designed and properly evaluated in order to avoid bias. In our study, two tools are taken as examples to further emphasize the data imbalance issue in CRISPR off-target prediction to achieve better sensitivity and specificity for optimized CRISPR gene editing. We would like to indicate that (1) the benchmark of CRISPR off-target prediction should be properly evaluated and not overestimated by considering data imbalance issue; (2) incorporation of efficient computational techniques (including ensemble learning and data synthesis techniques) can help to address the data imbalance issue and improve the performance of CRISPR off-target prediction. Taking together, we call for more efforts to address the data imbalance issue in CRISPR off-target prediction to facilitate clinical utility of CRISPR-based gene editing techniques.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based gene editing has been widely implemented in various cell types and organisms. A major challenge in the effective application of the CRISPR system is the need to design highly efficient single-guide RNA (sgRNA) with minimal off-target cleavage. Several tools are available for sgRNA design, while limited tools were compared. In our opinion, benchmarking the performance of the available tools and indicating their applicable scenarios are important issues. Moreover, whether the reported sgRNA design rules are reproducible across different sgRNA libraries, cell types and organisms remains unclear. In our study, a systematic and unbiased benchmark of the sgRNA predicting efficacy was performed on nine representative on-target design tools, based on six benchmark data sets covering five different cell types. The benchmark study presented here provides novel quantitative insights into the available CRISPR tools.