CRISPR-Cas systems not only play key roles in prokaryotic acquired immunity, but can also be adapted as powerful genome editing tools. Understanding the native role of CRISPR-Cas systems in providing adaptive immunity can lead to new CRISPR-based technologies. Here, we develop CRISPRminer, a knowledge base and web server to comprehensively collect and investigate the knowledge of CRISPR-Cas systems and generate instructive annotations, including CRISPR arrays and Cas protein annotation, CRISPR-Cas system classification, self-targeting events detection, microbe–phage interaction inference, and anti-CRISPR annotation. CRISPRminer is user-friendly and freely available at http://www.microbiome-bigdata.com/CRISPRminer.

Phage–microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage–microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage–host interaction signals (PHISs), which include clustered regularly interspaced short palindromic repeats (CRISPR) targeting, prophage, and protein–protein interaction signals. In the present study, we developed a novel tool phage–host interaction signal detector (PHISDetector) to predict phage–host interactions by detecting and integrating diverse in silico PHISs, and scoring the probability of phage–host interactions using machine learning models based on PHIS features. We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases. When tested on a dataset of 758 annotated phage–host pairs, PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels, respectively, outperforming other phage–host prediction tools. When applied to on 125,842 metagenomic viral contigs (mVCs) derived from 3042 geographically diverse samples, a detection rate of 54.54% could be achieved. Furthermore, PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant (MDR) bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health (NIH) Human Microbiome Project (HMP). The PHISDetector can be run either as a web server (http://www.microbiome-bigdata.com/PHISDetector/) for general users to study individual inputs or as a stand-alone version (https://github.com/HIT-ImmunologyLab/PHISDetector) to process massive phage contigs from virome studies.

As an intracellular form of a bacteriophage in the bacterial host genome, a prophage usually integrates into bacterial DNA with high specificity and contributes to horizontal gene transfer (HGT). With the exponentially increasing number of microbial sequences uncovered in genomic or metagenomics studies, there is a massive demand for a tool that is capable of fast and accurate identification of prophages. Here, we introduce DBSCAN-SWA, a command line software tool developed to predict prophage regions in bacterial genomes. DBSCAN-SWA runs faster than any previous tools. Importantly, it has great detection power based on analysis using 184 manually curated prophages, with a recall of 85% compared with Phage_Finder (63%), VirSorter (74%), and PHASTER (82%) for (Multi-) FASTA sequences. Moreover, DBSCAN-SWA outperforms the existing standalone prophage prediction tools for high-throughput sequencing data based on the analysis of 19,989 contigs of 400 bacterial genomes collected from Human Microbiome Project (HMP) project. DBSCAN-SWA also provides user-friendly result visualizations including a circular prophage viewer and interactive DataTables. DBSCAN-SWA is implemented in Python3 and is available under an open source GPLv2 license from https://github.com/HIT-ImmunologyLab/DBSCAN-SWA/.

The CRISPR-Cas system is a highly adaptive and RNA-guided immune system found in bacteria and archaea, which has applications as a genome editing tool and is a valuable system for studying the co-evolutionary dynamics of bacteriophage interactions. Here introduces CRISPRimmunity, a new web server designed for Acr prediction, identification of novel class 2 CRISPR-Cas loci, and dissection of key CRISPR-associated molecular events. CRISPRimmunity is built on a suite of CRISPR-oriented databases providing a comprehensive co-evolutionary perspective of the CRISPR-Cas and anti-CRISPR systems. The platform achieved a high prediction accuracy of 0.997 for Acr prediction when tested on a dataset of 99 experimentally validated Acrs and 676 non-Acrs, outperforming other existing prediction tools. Some of the newly identified class 2 CRISPR-Cas loci using CRISPRimmunity have been experimentally validated for cleavage activity in vitro. CRISPRimmunity offers the catalogues of pre-identified CRISPR systems to browse and query, the collected resources or databases to download, a well-designed graphical interface, a detailed tutorial, multi-faceted information, and exportable results in machine-readable formats, making it easy to use and facilitating future experimental design and further data mining. The platform is available at http://www.microbiome-bigdata.com/CRISPRimmunity. Moreover, the source code for batch analysis are published on Github (https://github.com/HIT-ImmunologyLab/CRISPRimmunity).

CRISPR-Cas systems and IS200/IS605 transposon-associated TnpBs have been utilized for the development of genome editing technologies. Using bioinformatics analysis and biochemical experiments, here we present a new family of RNA-guided DNA endonucleases. Our bioinformatics analysis initially identifies the stable co-occurrence of conserved RAGATH-18-derived RNAs (reRNAs) and their upstream IS607 TnpBs with an average length of 390 amino acids. IS607 TnpBs form programmable DNases through interaction with reRNAs. We discover the robust dsDNA interference activity of IS607 TnpB systems in bacteria and human cells. Further characterization of the Firmicutes bacteria IS607 TnpB system (ISFba1 TnpB) reveals that its dsDNA cleavage activity is remarkably sensitive to single mismatches between the guide and target sequences in human cells. Our findings demonstrate that a length of 20 nt in the guide sequence of reRNA achieves the highest DNA cleavage activity for ISFba1 TnpB. A cryo-EM structure of the ISFba1 TnpB effector protein bound by its cognate RAGATH-18 motif-containing reRNA and a dsDNA target reveals the mechanisms underlying reRNA recognition by ISFba1 TnpB, reRNA-guided dsDNA targeting, and the sensitivity of the ISFba1 TnpB system to base mismatches between the guide and target DNA. Collectively, this study identifies the IS607 TnpB family of compact and specific RNA-guided DNases with great potential for application in gene editing.