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      The defensome of complex bacterial communities

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          Abstract

          Bacteria have developed various defense mechanisms to avoid infection and killing in response to the fast evolution and turnover of viruses and other genetic parasites. Such pan-immune system ( defensome) encompasses a growing number of defense lines that include well-studied innate and adaptive systems such as restriction-modification, CRISPR-Cas and abortive infection, but also newly found ones whose mechanisms are still poorly understood. While the abundance and distribution of defense systems is well-known in complete and culturable genomes, there is a void in our understanding of their diversity and richness in complex microbial communities. Here we performed a large-scale in-depth analysis of the defensomes of 7759 high-quality bacterial population genomes reconstructed from soil, marine, and human gut environments. We observed a wide variation in the frequency and nature of the defensome among large phyla, which correlated with lifestyle, genome size, habitat, and geographic background. The defensome’s genetic mobility, its clustering in defense islands, and genetic variability was found to be system-specific and shaped by the bacterial environment. Hence, our results provide a detailed picture of the multiple immune barriers present in environmentally distinct bacterial communities and set the stage for subsequent identification of novel and ingenious strategies of diversification among uncultivated microbes.

          Abstract

          Bacteria have evolved numerous innate and adaptive defence mechanisms. Here, Beavogui et al characterise the impact of biogeography, genetic mobility, and clustering in defense islands, on the defence systems of soil, marine, and human gut bacterial populations genomes.

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              Prokka: rapid prokaryotic genome annotation.

              T Seemann (2014)
              The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. Prokka is implemented in Perl and is freely available under an open source GPLv2 license from http://vicbioinformatics.com/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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                Author and article information

                Contributors
                pcoutool@genoscope.cns.fr
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 March 2024
                8 March 2024
                2024
                : 15
                : 2146
                Affiliations
                [1 ]GRID grid.460789.4, ISNI 0000 0004 4910 6535, Génomique Métabolique, Genoscope, Institut François Jacob, Commissariat à l’Energie Atomique (CEA), CNRS, Université Evry, , Université Paris-Saclay, ; 2 Rue Gaston Crémieux, 91057 Evry, France
                [2 ]GRID grid.460789.4, ISNI 0000 0004 4910 6535, Genoscope, Institut François Jacob, CEA, , Université Paris-Saclay, ; 2 Rue Gaston Crémieux, 91057 Evry, France
                [3 ]Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022 / Tara GOsee, Paris, France
                [4 ]Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, ( https://ror.org/05a28rw58) Zürich, 8093 Switzerland
                [5 ]Institut Pasteur, Université Paris Cité, INSERM U1284, Molecular Diversity of Microbes lab, Paris, France
                Author information
                http://orcid.org/0000-0003-0771-8309
                http://orcid.org/0000-0001-7562-3454
                http://orcid.org/0000-0003-3161-8367
                Article
                46489
                10.1038/s41467-024-46489-0
                10924106
                38459056
                9f5ff840-11b1-43a6-b138-7419790762f2
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 August 2023
                : 28 February 2024
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                © Springer Nature Limited 2024

                Uncategorized
                metagenomics,computational biology and bioinformatics,bacterial genomics,environmental microbiology,microbiome

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