CRISPR-Cas is a tool that is widely used for gene editing. However, unexpected off-target effects may occur as a result of long-term nuclease activity. Anti-CRISPR proteins, which are powerful molecules that inhibit the CRISPR–Cas system, may have the potential to promote better utilization of the CRISPR-Cas system in gene editing, especially for gene therapy. Additionally, more in-depth research on these proteins would help researchers to better understand the co-evolution of bacteria and phages. Therefore, it is necessary to collect and integrate data on various types of anti-CRISPRs. Herein, data on these proteins were manually gathered through data screening of the literatures. Then, the first online resource, anti-CRISPRdb, was constructed for effectively organizing these proteins. It contains the available protein sequences, DNA sequences, coding regions, source organisms, taxonomy, virulence, protein interactors and their corresponding three-dimensional structures. Users can access our database at http://cefg.uestc.edu.cn/anti-CRISPRdb/ without registration. We believe that the anti-CRISPRdb can be used as a resource to facilitate research on anti-CRISPR proteins and in related fields.

Supplementary data are available at Bioinformatics online.

Genomic islands are genomic fragments of alien origin in bacterial and archaeal genomes, usually involved in symbiosis or pathogenesis. In this work, we described Zisland Explorer, a novel tool to predict genomic islands based on the segmental cumulative GC profile. Zisland Explorer was designed with a novel strategy, as well as a combination of the homogeneity and heterogeneity of genomic sequences. While the sequence homogeneity reflects the composition consistence within each island, the heterogeneity measures the composition bias between an island and the core genome. The performance of Zisland Explorer was evaluated on the data sets of 11 different organisms. Our results suggested that the true-positive rate (TPR) of Zisland Explorer was at least 10.3% higher than that of four other widely used tools. On the other hand, the new tool did not lose overall accuracy with the improvement in the TPR and showed better equilibrium among various evaluation indexes. Also, Zisland Explorer showed better accuracy in the prediction of experimental island data. Overall, the tool provides an alternative solution over other tools, which expands the field of island prediction and offers a supplement to increase the performance of the distinct predicting strategy. We have provided a web service as well as a graphical user interface and open-source code across multiple platforms for Zisland Explorer, which is available at http://cefg.uestc.edu.cn/Zisland_Explorer/ or http://tubic.tju.edu.cn/Zisland_Explorer/.

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.

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