CRISPR-Cas is a defense system of bacteria and archaea against phages. Parts of the foreign DNA, called spacers, are incorporated into the CRISPR array which constitutes the immune memory. The orientation of CRISPR arrays is crucial for analyzing and understanding the functionality of CRISPR systems and their targets. Several methods have been developed to identify the orientation of a CRISPR array. To predict the orientation, different methods use different features such as the repeat sequences between the spacers, the location of the leader sequence, the Cas genes, or PAMs. However, those features are often not sufficient to predict the orientation with certainty, or different methods disagree. Remarkably, almost all CRISPR systems have been found to insert spacers in a polarized manner at the leader end of the array. We introduce CRISPR-evOr, a method that leverages the resulting patterns to predict the acquisition orientation for (a group of) CRISPR arrays by reconstructing and comparing the likelihood of their evolutionary history with respect to both possible acquisition orientations. The new method is independent of Cas type, leader existence and location, and transcription orientation. CRISPR-evOr is thus particularly useful for arrays that other CRISPR orientation tools cannot predict confidently and to verify or resolve conflicting predictions from existing tools. CRISPR-evOr currently confidently predicts the orientation of 28.3% of the arrays in the considered subset of CRISPRCasdb, which other tools like CRISPRDirection and CRISPRstrand cannot reliably orient. As our tool leverages evolutionary information we expect this percentage to grow in the future when more closely related arrays will be available. Additionally, CRISPR-evOr provides confident decisions for rare subtypes of CRISPR arrays, where knowledge about repeats and leaders and their orientation is limited.

Small proteins encoded by short open reading frames (ORFs) with 50 codons or fewer are emerging as an important class of cellular macromolecules in diverse organisms. However, they often evade detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation in genomic regions previously thought to be non-coding, driving the development of ORF detection tools using Ribo-seq data. However, only a handful of tools have been designed for bacteria, and these have not yet been systematically compared. Here, we aimed to identify tools that use Ribo-seq data to correctly determine the translational status of annotated bacterial ORFs and also discover novel translated regions with high sensitivity. To this end, we generated a large set of annotated ORFs from four diverse bacterial organisms, manually labeled for their translation status based on Ribo-seq data, which are available for future benchmarking studies. This set was used to investigate the predictive performance of seven Ribo-seq-based ORF detection tools (REPARATION_blast, DeepRibo, Ribo-TISH, PRICE, smORFer, ribotricer and SPECtre), as well as IRSOM, which uses coding potential and RNA-seq coverage only. DeepRibo and REPARATION_blast robustly predicted translated ORFs, including sORFs, with no significant difference for ORFs in close proximity to other genes versus stand-alone genes. However, no tool predicted a set of novel, experimentally verified sORFs with high sensitivity. Start codon predictions with smORFer show the value of initiation site profiling data to further improve the sensitivity of ORF prediction tools in bacteria. Overall, we find that bacterial tools perform well for sORF detection, although there is potential for improving their performance, applicability, usability and reproducibility.

MOTIVATION: The CRISPR-Cas9 system is a Type II CRISPR system that has rapidly become the most versatile and widespread tool for genome engineering. It consists of two components, the Cas9 effector protein, and a single guide RNA that combines the spacer (for identifying the target) with the tracrRNA, a trans-activating small RNA required for both crRNA maturation and interference. While there are well-established methods for screening Cas effector proteins and CRISPR arrays, the detection of tracrRNA remains the bottleneck in detecting Class 2 CRISPR systems. RESULTS: We introduce a new pipeline CRISPRtracrRNA for screening and evaluation of tracrRNA candidates in genomes. This pipeline combines evidence from different components of the Cas9-sgRNA complex. The core is a newly developed structural model via covariance models from a sequence-structure alignment of experimentally validated tracrRNAs. As additional evidence, we determine the terminator signal (required for the tracrRNA transcription) and the RNA-RNA interaction between the CRISPR array repeat and the 5'-part of the tracrRNA. Repeats are detected via an ML-based approach (CRISPRidenify). Providing further evidence, we detect the cassette containing the Cas9 (Type II CRISPR systems) and Cas12 (Type V CRISPR systems) effector protein. Our tool is the first for detecting tracrRNA for Type V systems. AVAILABILITY AND IMPLEMENTATION: The implementation of the CRISPRtracrRNA is available on GitHub upon requesting the access permission, (https://github.com/BackofenLab/CRISPRtracrRNA). Data generated in this study can be obtained upon request to the corresponding person: Rolf Backofen ([email protected]). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: CRISPR-Cas are important systems found in most archaeal and many bacterial genomes, providing adaptive immunity against mobile genetic elements in prokaryotes. The CRISPR-Cas systems are encoded by a set of consecutive cas genes, here termed cassette. The identification of cassette boundaries is key for finding cassettes in CRISPR research field. This is often carried out by using Hidden Markov Models and manual annotation. In this article, we propose the first method able to automatically define the cassette boundaries. In addition, we present a Cas-type predictive model used by the method to assign each gene located in the region defined by a cassette's boundaries a Cas label from a set of pre-defined Cas types. Furthermore, the proposed method can detect potentially new cas genes and decompose a cassette into its modules. RESULTS: We evaluate the predictive performance of our proposed method on data collected from the two most recent CRISPR classification studies. In our experiments, we obtain an average similarity of 0.86 between the predicted and expected cassettes. Besides, we achieve F-scores above 0.9 for the classification of cas genes of known types and 0.73 for the unknown ones. Finally, we conduct two additional study cases, where we investigate the occurrence of potentially new cas genes and the occurrence of module exchange between different genomes. AVAILABILITY AND IMPLEMENTATION: https://github.com/BackofenLab/Casboundary. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The CRISPR-Cas system is an adaptive immune system in many archaea and bacteria, which provides resistance against invading genetic elements. The first phase of CRISPR-Cas immunity is called adaptation, in which small DNA fragments are excised from genetic elements and are inserted into a CRISPR array generally adjacent to its so called leader sequence at one end of the array. It has been shown that transcription initiation and adaptation signals of the CRISPR array are located within the leader. However, apart from promoters, there is very little knowledge of sequence or structural motifs or their possible functions. Leader properties have mainly been characterized through transcriptional initiation data from single organisms but large-scale characterization of leaders has remained challenging due to their low level of sequence conservation. RESULTS: We developed a method to successfully detect leader sequences by focusing on the consensus repeat of the adjacent CRISPR array and weak upstream conservation signals. We applied our tool to the analysis of a comprehensive genomic database and identified several characteristic properties of leader sequences specific to archaea and bacteria, ranging from distinctive sizes to preferential indel localization. CRISPRleader provides a full annotation of the CRISPR array, its strand orientation as well as conserved core leader boundaries that can be uploaded to any genome browser. In addition, it outputs reader-friendly HTML pages for conserved leader clusters from our database. AVAILABILITY AND IMPLEMENTATION: CRISPRleader and multiple sequence alignments for all 195 leader clusters are available at http://www.bioinf.uni-freiburg.de/Software/CRISPRleader/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs. RESULTS: We present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising >4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0. AVAILABILITY: CRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.