ArticleBioinformatics (Oxford, England)2021
Casboundary: automated definition of integral Cas cassettes.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine-learning in optimization of CRISPR technology.Machine learning. Health · 2026Review
- Comprehensive genome-based characterization and pan-genome analysis of Bacillus paranthracis SM02 isolated from a fermented food ecosystem.Functional & integrative genomics · 2026Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- Discovery of CRISPR-Cas12a clades using a large language model.Nature communications · 2025Article
- Article
- CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems.Nucleic acids research · 2021Article
- VISPR-online: a web-based interactive tool to visualize CRISPR screening experiments.BMC bioinformatics · 2021Article
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Authors and funding
6 authors.
Funding
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Abstract
motivationCRISPR-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.
resultsWe 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.
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