The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR–Cas9 guide design.

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) and gas chromatography-high resolution mass spectrometry (GC-HRMS) have revolutionized analytical chemistry among many other disciplines. These advanced instrumentations allow to theoretically capture the whole chemical universe that is contained in samples, giving unimaginable opportunities to the scientific community. Laboratories equipped with these instruments produce a lot of data daily that can be digitally archived. Digital storage of data opens up the opportunity for retrospective suspect screening investigations for the occurrence of chemicals in the stored chromatograms. The first step of this approach involves the prediction of which data is more appropriate to be searched. In this study, we built an optimized multi-label classifier for predicting the most appropriate instrumental method (LC-HRMS or GC-HRMS or both) for the analysis of chemicals in digital specimens. The approach involved the generation of a baseline model based on the knowledge that an expert would use and the generation of an optimized machine learning model. A multi-step feature selection approach, a model selection strategy, and optimization of the classifier’s hyperparameters led to a model with accuracy that outperformed the baseline implementation. The models were used to predict the most appropriate instrumental technique for new substances. The scripts are available at GitHub and the dataset at Zenodo.

The development of the CRISPR-Cas9 technology has provided a simple yet powerful system for genome editing. Current gRNA design tools serve as an important platform for the efficient application of the CRISPR systems. However, most of the existing tools are black-box models that suffer from limitations, such as variable performance and unclear mechanism of decision making. Here, we introduce CRISPRedict, an interpretable gRNA efficiency prediction model for CRISPR-Cas9 gene editing. Its strength lies in the fact that it can accurately predict efficient guide RNAs—with equivalent performance to state-of-the-art tools—while being a simple linear model. Implemented as a user-friendly web server, CRISPRedict offers (i) quick and accurate predictions across various experimental conditions (e.g. U6/T7 transcription); (ii) regression and classification models for scoring gRNAs and (iii) multiple visualizations to explain the obtained results. Given its performance, interpretability, and versatility, we expect that it will assist researchers in the gRNA design process and facilitate genome editing research. CRISPRedict is available for use at http://www.crispredict.org/.

The development of the CRISPR-Cas9 technology has provided a simple yet powerful system for targeted genome editing. Compared with previous gene-editing tools, the CRISPR-Cas9 system identifies target sites by the complementarity between the guide RNA (gRNA) and the DNA sequence, which is less expensive and time-consuming, as well as more precise and scalable. To effectively apply the CRISPR-Cas9 system, researchers need to identify target sites that can be cleaved efficiently and for which the candidate gRNAs have little or no cleavage at other genomic locations. For this reason, numerous computational approaches have been developed to predict cleavage efficiency and exclude undesirable targets. However, current design tools cannot robustly predict experimental success as prediction accuracy depends on the assumptions of the underlying model and how closely the experimental setup matches the training data. Moreover, the most successful tools implement complex machine learning and deep learning models, leading to predictions that are not easily interpretable. Here, we introduce CRISPRedict, a simple linear model that provides accurate and interpretable predictions for guide design. Comprehensive evaluation on twelve independent datasets demonstrated that CRISPRedict has an equivalent performance with the currently most accurate tools and outperforms the remaining ones. Moreover, it has the most robust performance for both U6 and T7 data, illustrating its applicability to tasks under different conditions. Therefore, our system can assist researchers in the gRNA design process by providing accurate and explainable predictions. These predictions can then be used to guide genome editing experiments and make plausible hypotheses for further investigation. The source code of CRISPRedict along with instructions for use is available at https://github.com/VKonstantakos/CRISPRedict.

Background and purpose In recent years, the use of coiling has gained increased popularity for the treatment of intracranial aneurysms, and stroke physicians are confronted with rare pathologies associated with this relatively new and evolving treatment method, such as embolization of pieces of the polymeric filaments from the coils and a subsequent inflammatory response. In particular, white matter enhancing lesions are a rare complication after aneurysm endovascular therapy (EVT), suggesting a foreign body reaction to shedding of hydrophilic coating from the endovascular devices into the blood stream. The description of such a case aims to raise the clinicians' awareness of the symptomatic delayed and recurring inflammatory changes that may occur after endovascular aneurysmal treatment with the use of coiling devices. Case description A 64‐year‐old woman underwent coiling of a ruptured right posterior communicating artery aneurysm. She was asymptomatic after EVT. One year later, she presented with headache, acoustic hallucinations, paresthesias and left arm weakness. Brain magnetic resonance imaging (MRI) revealed multiple enhancing white matter lesions in the right hemisphere. She was treated with pulse intravenous methylprednisolone, followed by oral prednisolone; all clinical symptoms resolved and imaging findings improved substantially. Two years after tapering the steroids, follow‐up symptoms recurred and repeat brain MRI revealed new enhancing white matter lesions. Discussion and conclusions There is an increasing number of similar reports of enhancing white matter lesions after coiling of intracranial aneurysms, with the incidence estimated to be between 0.5% and 2.3% in different cohort studies. Close monitoring for the appearance of new neurologic symptoms that could suggest delayed brain reactivity should be recommended.