Gene Expression-Based Cancer Classification for Handling the Class Imbalance Problem and Curse of Dimensionality.

Cancer is a leading cause of death globally. The majority of cancer cases are only diagnosed in the late stages of cancer due to the use of conventional methods. This reduces the chance of survival for cancer patients. Therefore, early detection consequently followed by early diagnoses are important tasks in cancer research. Gene expression microarray technology has been applied to detect and diagnose most types of cancers in their early stages and has gained encouraging results. In this paper, we address the problem of classifying cancer based on gene expression for handling the class imbalance problem and the curse of dimensionality. The oversampling technique is utilized to overcome this problem by adding synthetic samples. Another common issue related to the gene expression dataset addressed in this paper is the curse of dimensionality. This problem is addressed by applying chi-square and information gain feature selection techniques. After applying these techniques individually, we proposed a method to select the most significant genes by combining those two techniques (CHiS and IG). We investigated the effect of these techniques individually and in combination. Four benchmarking biomedical datasets (Leukemia-subtypes, Leukemia-ALLAML, Colon, and CuMiDa) were used. The experimental results reveal that the oversampling techniques improve the results in most cases. Additionally, the performance of the proposed feature selection technique outperforms individual techniques in nearly all cases. In addition, this study provides an empirical study for evaluating several oversampling techniques along with ensemble-based learning. The experimental results also reveal that SVM-SMOTE, along with the random forests classifier, achieved the highest results, with a reporting accuracy of 100%. The obtained results surpass the findings in the existing literature as well.

Interrogating two extensively self-targeting Type I CRISPR-Cas systems in Xanthomonas albilineans reveals distinct anti-CRISPR proteins that block DNA degradation.

CRISPR-Cas systems store fragments of invader DNA as spacers to recognize and clear those same invaders in the future. Spacers can also be acquired from the host's genomic DNA, leading to lethal self-targeting. While self-targeting can be circumvented through different mechanisms, natural examples remain poorly explored. Here, we investigate extensive self-targeting by two CRISPR-Cas systems encoding 24 self-targeting spacers in the plant pathogen Xanthomonas albilineans. We show that the native I-C and I-F1 systems are actively expressed and that CRISPR RNAs are properly processed. When expressed in Escherichia coli, each Cascade complex binds its PAM-flanked DNA target to block transcription, while the addition of Cas3 paired with genome targeting induces cell killing. While exploring how X. albilineans survives self-targeting, we predicted putative anti-CRISPR proteins (Acrs) encoded within the bacterium's genome. Screening of identified candidates with cell-free transcription-translation systems and in E. coli revealed two Acrs, which we named AcrIC11 and AcrIF12Xal, that inhibit the activity of Cas3 but not Cascade of the respective system. While AcrF12Xal is homologous to AcrIF12, AcrIC11 shares sequence and structural homology with the anti-restriction protein KlcA. These findings help explain tolerance of self-targeting through two CRISPR-Cas systems and expand the known suite of DNA degradation-inhibiting Acrs.

Revealing the small proteome of Haloferax volcanii by combining ribosome profiling and small-protein optimized mass spectrometry.

In contrast to extensively studied prokaryotic 'small' transcriptomes (encompassing all small noncoding RNAs), small proteomes (here defined as including proteins ≤70 aa) are only now entering the limelight. The absence of a complete small protein catalogue in most prokaryotes precludes our understanding of how these molecules affect physiology. So far, archaeal genomes have not yet been analyzed broadly with a dedicated focus on small proteins. Here, we present a combinatorial approach, integrating experimental data from small protein-optimized mass spectrometry (MS) and ribosome profiling (Ribo-seq), to generate a high confidence inventory of small proteins in the model archaeon Haloferax volcanii. We demonstrate by MS and Ribo-seq that 67% of the 317 annotated small open reading frames (sORFs) are translated under standard growth conditions. Furthermore, annotation-independent analysis of Ribo-seq data showed ribosomal engagement for 47 novel sORFs in intergenic regions. A total of seven of these were also detected by proteomics, in addition to an eighth novel small protein solely identified by MS. We also provide independent experimental evidence in vivo for the translation of 12 sORFs (annotated and novel) using epitope tagging and western blotting, underlining the validity of our identification scheme. Several novel sORFs are conserved in Haloferax species and might have important functions. Based on our findings, we conclude that the small proteome of H. volcanii is larger than previously appreciated, and that combining MS with Ribo-seq is a powerful approach for the discovery of novel small protein coding genes in archaea.

Current Bioinformatics Tools to Optimize CRISPR/Cas9 Experiments to Reduce Off-Target Effects.

The CRISPR-Cas system has evolved into a cutting-edge technology that has transformed the field of biological sciences through precise genetic manipulation. CRISPR/Cas9 nuclease is evolving into a revolutionizing method to edit any gene of any species with desirable outcomes. The swift advancement of CRISPR-Cas technology is reflected in an ever-expanding ecosystem of bioinformatics tools designed to make CRISPR/Cas9 experiments easier. To assist researchers with efficient guide RNA designs with fewer off-target effects, nuclease target site selection, and experimental validation, bioinformaticians have built and developed a comprehensive set of tools. In this article, we will review the various computational tools available for the assessment of off-target effects, as well as the quantification of nuclease activity and specificity, including web-based search tools and experimental methods, and we will describe how these tools can be optimized for gene knock-out (KO) and gene knock-in (KI) for model organisms. We also discuss future directions in precision genome editing and its applications, as well as challenges in target selection, particularly in predicting off-target effects.

CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection.

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 (backofen@informatik.uni-freiburg.de). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases.

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages whereas anti-CRISPR proteins (Acrs) in phages inhibit CRISPR defenses. Known Acrs have proven extremely diverse, complicating their identification. Here, we report a deep learning algorithm for Acr identification that revealed an Acr against type VI-B CRISPR-Cas systems. The algorithm predicted numerous putative Acrs spanning almost all CRISPR-Cas types and subtypes, including over 7,000 putative type IV and VI Acrs not predicted by other algorithms. By performing a cell-free screen for Acr hits against type VI-B systems, we identified a potent inhibitor of Cas13b nucleases we named AcrVIB1. AcrVIB1 blocks Cas13b-mediated defense against a targeted plasmid and lytic phage, and its inhibitory function principally occurs upstream of ribonucleoprotein complex formation. Overall, our work helps expand the known Acr universe, aiding our understanding of the bacteria-phage arms race and the use of Acrs to control CRISPR technologies.

Spacer prioritization in CRISPR-Cas9 immunity is enabled by the leader RNA.

CRISPR-Cas systems store fragments of foreign DNA, called spacers, as immunological recordings used to combat future infections. Of the many spacers stored in a CRISPR array, the most recent are known to be prioritized for immune defence. However, the underlying mechanism remains unclear. Here we show that the leader region upstream of CRISPR arrays in CRISPR-Cas9 systems enhances CRISPR RNA (crRNA) processing from the newest spacer, prioritizing defence against the matching invader. Using the CRISPR-Cas9 system from Streptococcus pyogenes as a model, we found that the transcribed leader interacts with the conserved repeats bordering the newest spacer. The resulting interaction promotes transactivating crRNA (tracrRNA) hybridization with the second of the two repeats, accelerating crRNA processing. Accordingly, disruption of this structure reduces the abundance of the associated crRNA and immune defence against targeted plasmids and bacteriophages. Beyond the S. pyogenes system, bioinformatics analyses revealed that leader-repeat structures appear across CRISPR-Cas9 systems. CRISPR-Cas systems thus possess an RNA-based mechanism to prioritize defence against the most recently encountered invaders.

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria.

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.

CdrS Is a Global Transcriptional Regulator Influencing Cell Division in Haloferax volcanii.

Transcriptional regulators that integrate cellular and environmental signals to control cell division are well known in bacteria and eukaryotes, but their existence is poorly understood in archaea. We identified a conserved gene (cdrS) that encodes a small protein and is highly transcribed in the model archaeon Haloferax volcanii. The cdrS gene could not be deleted, but CRISPR interference (CRISPRi)-mediated repression of the cdrS gene caused slow growth and cell division defects and changed the expression of multiple genes and their products associated with cell division, protein degradation, and metabolism. Consistent with this complex regulatory network, overexpression of cdrS inhibited cell division, whereas overexpression of the operon encoding both CdrS and a tubulin-like cell division protein (FtsZ2) stimulated division. Chromatin immunoprecipitation-DNA sequencing (ChIP-Seq) identified 18 DNA-binding sites of the CdrS protein, including one upstream of the promoter for a cell division gene, ftsZ1, and another upstream of the essential gene dacZ, encoding diadenylate cyclase involved in c-di-AMP signaling, which is implicated in the regulation of cell division. These findings suggest that CdrS is a transcription factor that plays a central role in a regulatory network coordinating metabolism and cell division. IMPORTANCE Cell division is a central mechanism of life and is essential for growth and development. Members of the Bacteria and Eukarya have different mechanisms for cell division, which have been studied in detail. In contrast, cell division in members of the Archaea is still understudied, and its regulation is poorly understood. Interestingly, different cell division machineries appear in members of the Archaea, with the Euryarchaeota using a cell division apparatus based on the tubulin-like cytoskeletal protein FtsZ, as in bacteria. Here, we identify the small protein CdrS as essential for survival and a central regulator of cell division in the euryarchaeon Haloferax volcanii. CdrS also appears to coordinate other cellular pathways, including synthesis of signaling molecules and protein degradation. Our results show that CdrS plays a sophisticated role in cell division, including regulation of numerous associated genes. These findings are expected to initiate investigations into conditional regulation of division in archaea.

CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems.

CRISPR-Cas systems are adaptive immune systems in prokaryotes, providing resistance against invading viruses and plasmids. The identification of CRISPR loci is currently a non-standardized, ambiguous process, requiring the manual combination of multiple tools, where existing tools detect only parts of the CRISPR-systems, and lack quality control, annotation and assessment capabilities of the detected CRISPR loci. Our CRISPRloci server provides the first resource for the prediction and assessment of all possible CRISPR loci. The server integrates a series of advanced Machine Learning tools within a seamless web interface featuring: (i) prediction of all CRISPR arrays in the correct orientation; (ii) definition of CRISPR leaders for each locus; and (iii) annotation of cas genes and their unambiguous classification. As a result, CRISPRloci is able to accurately determine the CRISPR array and associated information, such as: the Cas subtypes; cassette boundaries; accuracy of the repeat structure, orientation and leader sequence; virus-host interactions; self-targeting; as well as the annotation of cas genes, all of which have been missing from existing tools. This annotation is presented in an interactive interface, making it easy for scientists to gain an overview of the CRISPR system in their organism of interest. Predictions are also rendered in GFF format, enabling in-depth genome browser inspection. In summary, CRISPRloci constitutes a full suite for CRISPR-Cas system characterization that offers annotation quality previously available only after manual inspection.

CRISPRidentify: identification of CRISPR arrays using machine learning approach.

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRidentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

Casboundary: automated definition of integral Cas cassettes.

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.

Adaptation induced by self-targeting in a type I-B CRISPR-Cas system.

Haloferax volcanii is, to our knowledge, the only prokaryote known to tolerate CRISPR-Cas-mediated damage to its genome in the WT background; the resulting cleavage of the genome is repaired by homologous recombination restoring the WT version. In mutant Haloferax strains with enhanced self-targeting, cell fitness decreases and microhomology-mediated end joining becomes active, generating deletions in the targeted gene. Here we use self-targeting to investigate adaptation in H. volcanii CRISPR-Cas type I-B. We show that self-targeting and genome breakage events that are induced by self-targeting, such as those catalyzed by active transposases, can generate DNA fragments that are used by the CRISPR-Cas adaptation machinery for integration into the CRISPR loci. Low cellular concentrations of self-targeting crRNAs resulted in acquisition of large numbers of spacers originating from the entire genomic DNA. In contrast, high concentrations of self-targeting crRNAs resulted in lower acquisition that was mostly centered on the targeting site. Furthermore, we observed naïve spacer acquisition at a low level in WT Haloferax cells and with higher efficiency upon overexpression of the Cas proteins Cas1, Cas2, and Cas4. Taken together, these findings indicate that naïve adaptation is a regulated process in H. volcanii that operates at low basal levels and is induced by DNA breaks.

CRISPRcasIdentifier: Machine learning for accurate identification and classification of CRISPR-Cas systems.

BACKGROUND: CRISPR-Cas genes are extraordinarily diverse and evolve rapidly when compared to other prokaryotic genes. With the rapid increase in newly sequenced archaeal and bacterial genomes, manual identification of CRISPR-Cas systems is no longer viable. Thus, an automated approach is required for advancing our understanding of the evolution and diversity of these systems and for finding new candidates for genome engineering in eukaryotic models. RESULTS: We introduce CRISPRcasIdentifier, a new machine learning-based tool that combines regression and classification models for the prediction of potentially missing proteins in instances of CRISPR-Cas systems and the prediction of their respective subtypes. In contrast to other available tools, CRISPRcasIdentifier can both detect cas genes and extract potential association rules that reveal functional modules for CRISPR-Cas systems. In our experimental benchmark on the most recently published and comprehensive CRISPR-Cas system dataset, CRISPRcasIdentifier was compared with recent and state-of-the-art tools. According to the experimental results, CRISPRcasIdentifier presented the best Cas protein identification and subtype classification performance. CONCLUSIONS: Overall, our tool greatly extends the classification of CRISPR cassettes and, for the first time, predicts missing Cas proteins and association rules between Cas proteins. Additionally, we investigated the properties of CRISPR subtypes. The proposed tool relies not only on the knowledge of manual CRISPR annotation but also on models trained using machine learning.

Evolutionary classification of CRISPR-Cas systems: a burst of class 2 and derived variants.

The number and diversity of known CRISPR-Cas systems have substantially increased in recent years. Here, we provide an updated evolutionary classification of CRISPR-Cas systems and cas genes, with an emphasis on the major developments that have occurred since the publication of the latest classification, in 2015. The new classification includes 2 classes, 6 types and 33 subtypes, compared with 5 types and 16 subtypes in 2015. A key development is the ongoing discovery of multiple, novel class 2 CRISPR-Cas systems, which now include 3 types and 17 subtypes. A second major novelty is the discovery of numerous derived CRISPR-Cas variants, often associated with mobile genetic elements that lack the nucleases required for interference. Some of these variants are involved in RNA-guided transposition, whereas others are predicted to perform functions distinct from adaptive immunity that remain to be characterized experimentally. The third highlight is the discovery of numerous families of ancillary CRISPR-linked genes, often implicated in signal transduction. Together, these findings substantially clarify the functional diversity and evolutionary history of CRISPR-Cas.

CRISPR-Cas bioinformatics.

Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated proteins (Cas) are essential genetic elements in many archaeal and bacterial genomes, playing a key role in a prokaryote adaptive immune system against invasive foreign elements. In recent years, the CRISPR-Cas system has also been engineered to facilitate target gene editing in eukaryotic genomes. Bioinformatics played an essential role in the detection and analysis of CRISPR systems and here we review the bioinformatics-based efforts that pushed the field of CRISPR-Cas research further. We discuss the bioinformatics tools that have been published over the last few years and, finally, present the most popular tools for the design of CRISPR-Cas9 guides.

The nuts and bolts of the Haloferax CRISPR-Cas system I-B.

Invading genetic elements pose a constant threat to prokaryotic survival, requiring an effective defence. Eleven years ago, the arsenal of known defence mechanisms was expanded by the discovery of the CRISPR-Cas system. Although CRISPR-Cas is present in the majority of archaea, research often focuses on bacterial models. Here, we provide a perspective based on insights gained studying CRISPR-Cas system I-B of the archaeon Haloferax volcanii. The system relies on more than 50 different crRNAs, whose stability and maintenance critically depend on the proteins Cas5 and Cas7, which bind the crRNA and form the Cascade complex. The interference machinery requires a seed sequence and can interact with multiple PAM sequences. H. volcanii stands out as the first example of an organism that can tolerate autoimmunity via the CRISPR-Cas system while maintaining a constitutively active system. In addition, the H. volcanii system was successfully developed into a tool for gene regulation.

Cross-cleavage activity of Cas6b in crRNA processing of two different CRISPR-Cas systems in Methanosarcina mazei Gö1.

The clustered regularly interspaced short palindromic repeat (CRISPR) system is a prokaryotic adaptive defense system against foreign nucleic acids. In the methanoarchaeon Methanosarcina mazei Gö1, two types of CRISPR-Cas systems are present (type I-B and type III-C). Both loci encode a Cas6 endonuclease, Cas6b-IB and Cas6b-IIIC, typically responsible for maturation of functional short CRISPR RNAs (crRNAs). To evaluate potential cross cleavage activity, we biochemically characterized both Cas6b proteins regarding their crRNA binding behavior and their ability to process pre-crRNA from the respective CRISPR array in vivo. Maturation of crRNA was studied in the respective single deletion mutants by northern blot and RNA-Seq analysis demonstrating that in vivo primarily Cas6b-IB is responsible for crRNA processing of both CRISPR arrays. Tentative protein level evidence for the translation of both Cas6b proteins under standard growth conditions was detected, arguing for different activities or a potential non-redundant role of Cas6b-IIIC within the cell. Conservation of both Cas6 endonucleases was observed in several other M. mazei isolates, though a wide variety was displayed. In general, repeat and leader sequence conservation revealed a close correlation in the M. mazei strains. The repeat sequences from both CRISPR arrays from M. mazei Gö1 contain the same sequence motif with differences only in two nucleotides. These data stand in contrast to all other analyzed M. mazei isolates, which have at least one additional CRISPR array with repeats belonging to another sequence motif. This conforms to the finding that Cas6b-IB is the crucial and functional endonuclease in M. mazei Gö1. Abbreviations: sRNA: small RNA; crRNA: CRISPR RNA; pre-crRNAs: Precursor CRISPR RNA; CRISPR: clustered regularly interspaced short palindromic repeats; Cas: CRISPR associated; nt: nucleotide; RNP: ribonucleoprotein; RBS: ribosome binding site.

Comprehensive search for accessory proteins encoded with archaeal and bacterial type III CRISPR-cas gene cassettes reveals 39 new cas gene families.

A study was undertaken to identify conserved proteins that are encoded adjacent to cas gene cassettes of Type III CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR associated) interference modules. Type III modules have been shown to target and degrade dsDNA, ssDNA and ssRNA and are frequently intertwined with cofunctional accessory genes, including genes encoding CRISPR-associated Rossman Fold (CARF) domains. Using a comparative genomics approach, and defining a Type III association score accounting for coevolution and specificity of flanking genes, we identified and classified 39 new Type III associated gene families. Most archaeal and bacterial Type III modules were seen to be flanked by several accessory genes, around half of which did not encode CARF domains and remain of unknown function. Northern blotting and interference assays in Synechocystis confirmed that one particular non-CARF accessory protein family was involved in crRNA maturation. Non-CARF accessory genes were generally diverse, encoding nuclease, helicase, protease, ATPase, transporter and transmembrane domains with some encoding no known domains. We infer that additional families of non-CARF accessory proteins remain to be found. The method employed is scalable for potential application to metagenomic data once automated pipelines for annotation of CRISPR-Cas systems have been developed. All accessory genes found in this study are presented online in a readily accessible and searchable format for researchers to audit their model organism of choice: http://accessory.crispr.dk .

CRISPR-Cas systems in multicellular cyanobacteria.

Novel CRISPR-Cas systems possess substantial potential for genome editing and manipulation of gene expression. The types and numbers of CRISPR-Cas systems vary substantially between different organisms. Some filamentous cyanobacteria harbor > 40 different putative CRISPR repeat-spacer cassettes, while the number of cas gene instances is much lower. Here we addressed the types and diversity of CRISPR-Cas systems and of CRISPR-like repeat-spacer arrays in 171 publicly available genomes of multicellular cyanobacteria. The number of 1328 repeat-spacer arrays exceeded the total of 391 encoded Cas1 proteins suggesting a tendency for fragmentation or the involvement of alternative adaptation factors. The model cyanobacterium Anabaena sp. PCC 7120 contains only three cas1 genes but hosts three Class 1, possibly one Class 2 and five orphan repeat-spacer arrays, all of which exhibit crRNA-typical expression patterns suggesting active transcription, maturation and incorporation into CRISPR complexes. The CRISPR-Cas system within the element interrupting the Anabaena sp. PCC 7120 fdxN gene, as well as analogous arrangements in other strains, occupy the genetic elements that become excised during the differentiation-related programmed site-specific recombination. This fact indicates the propensity of these elements for the integration of CRISPR-cas systems and points to a previously not recognized connection. The gene all3613 resembling a possible Class 2 effector protein is linked to a short repeat-spacer array and a single tRNA gene, similar to its homologs in other cyanobacteria. The diversity and presence of numerous CRISPR-Cas systems in DNA elements that are programmed for homologous recombination make filamentous cyanobacteria a prolific resource for their study. Abbreviations: Cas: CRISPR associated sequences; CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats; C2c: Class 2 candidate; SDR: small dispersed repeat; TSS: transcriptional start site; UTR: untranslated region.