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      Distribution of specific prokaryotic immune systems correlates with host optimal growth temperature

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      NAR Genomics and Bioinformatics
      Oxford University Press

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          Abstract

          Prokaryotes encode an arsenal of highly diverse immune systems to protect themselves against invading nucleic acids such as viruses, plasmids and transposons. This includes invader-interfering systems that neutralize invaders to protect their host, and abortive-infection systems, which trigger dormancy or cell death in their host to offer population-level immunity. Most prokaryotic immune systems are found across different environments and prokaryotic phyla, but their distribution appears biased and the factors that influence their distribution are largely unknown. Here, we compared and combined the prokaryotic immune system identification tools DefenseFinder and PADLOC to obtain an expanded view of the immune system arsenal. Our results show that the number of immune systems encoded is positively correlated with genome size and that the distribution of specific immune systems is linked to phylogeny. Furthermore, we reveal that certain invader-interfering systems are more frequently encoded by hosts with a relatively high optimum growth temperature, while abortive-infection systems are generally more frequently encoded by hosts with a relatively low optimum growth temperature. Combined, our study reveals several factors that correlate with differences in the distribution of prokaryotic immune systems and extends our understanding of how prokaryotes protect themselves from invaders in different environments.

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          Most cited references74

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          Prodigal: prokaryotic gene recognition and translation initiation site identification

          Background The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. Results With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. Conclusion We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.
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            Biopython: freely available Python tools for computational molecular biology and bioinformatics

            Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license. Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/_Mailing_lists peter.cock@scri.ac.uk.
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              Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors

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                Author and article information

                Contributors
                Journal
                NAR Genom Bioinform
                NAR Genom Bioinform
                nargab
                NAR Genomics and Bioinformatics
                Oxford University Press
                2631-9268
                September 2024
                20 August 2024
                20 August 2024
                : 6
                : 3
                : lqae105
                Affiliations
                Laboratory of Biochemistry, Wageningen University , Wageningen, Stippeneng 4, 6708WE, the Netherlands
                Laboratory of Biochemistry, Wageningen University , Wageningen, Stippeneng 4, 6708WE, the Netherlands
                Laboratory of Biochemistry, Wageningen University , Wageningen, Stippeneng 4, 6708WE, the Netherlands
                Author notes
                To whom correspondence should be addressed. Email: daan.swarts@ 123456wur.nl
                Author information
                https://orcid.org/0000-0003-4378-141X
                https://orcid.org/0000-0003-4412-9191
                Article
                lqae105
                10.1093/nargab/lqae105
                11333966
                ec827233-aad2-4013-ae0f-11305e06a384
                © The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 23 May 2024
                : 15 July 2024
                : 2 August 2024
                Page count
                Pages: 12
                Funding
                Funded by: The Graduate School of Experimental Plant Sciences;
                Award ID: EPS
                Award ID: EPS-1 036
                Funded by: Netherlands Organization for Scientific Research (NWO) VENI;
                Award ID: 016.Veni.192.072
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: ERC-2020-STG 948783
                Categories
                AcademicSubjects/SCI00030
                AcademicSubjects/SCI00980
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI01180
                Standard Article

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