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      Expansion of Armatimonadota through marine sediment sequencing describes two classes with unique ecological roles

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          Abstract

          Marine sediments comprise one of the largest environments on the planet, and their microbial inhabitants are significant players in global carbon and nutrient cycles. Recent studies using metagenomic techniques have shown the complexity of these communities and identified novel microorganisms from the ocean floor. Here, we obtained 77 metagenome-assembled genomes (MAGs) from the bacterial phylum Armatimonadota in the Guaymas Basin, Gulf of California, and the Bohai Sea, China. These MAGs comprise two previously undescribed classes within Armatimonadota, which we propose naming Hebobacteria and Zipacnadia. They are globally distributed in hypoxic and anoxic environments and are dominant members of deep-sea sediments (up to 1.95% of metagenomic raw reads). The classes described here also have unique metabolic capabilities, possessing pathways to reduce carbon dioxide to acetate via the Wood-Ljungdahl pathway (WLP) and generating energy through the oxidative branch of glycolysis using carbon dioxide as an electron sink, maintaining the redox balance using the WLP. Hebobacteria may also be autotrophic, not previously identified in Armatimonadota. Furthermore, these Armatimonadota may play a role in sulfur and nitrogen cycling, using the intermediate compounds hydroxylamine and sulfite. Description of these MAGs enhances our understanding of diversity and metabolic potential within anoxic habitats worldwide.

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          Fast and sensitive protein alignment using DIAMOND.

          The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.
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            CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

            Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.
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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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                Author and article information

                Contributors
                acidophile@gmail.com
                valdeanda@gmail.com
                Journal
                ISME Commun
                ISME Commun
                ISME Communications
                Nature Publishing Group UK (London )
                2730-6151
                2730-6151
                24 June 2023
                24 June 2023
                2023
                : 3
                : 64
                Affiliations
                [1 ]GRID grid.89336.37, ISNI 0000 0004 1936 9924, Department of Marine Science, , University of Texas at Austin, Marine Science Institute, ; Port Aransas, TX USA
                [2 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Bacteriology, , University of Wisconsin-Madison, ; Madison, WI USA
                [3 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Integrative Biology, , University of Wisconsin-Madison, ; Madison, WI USA
                [4 ]GRID grid.27255.37, ISNI 0000 0004 1761 1174, Institute of Marine Science and Technology, , Shandong University, ; Qingdao, China
                [5 ]GRID grid.89336.37, ISNI 0000 0004 1936 9924, Department of Integrative Biology, , University of Texas at Austin, ; Austin, TX USA
                [6 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Unidad Académica de Ecologia y Biodiversidad Acuática, Instituto de Ciencias del Mar y Limnologia, , Universidad Nacional Autónoma de Mexico, ; Mexico City, Mexico
                [7 ]GRID grid.4709.a, ISNI 0000 0004 0495 846X, EMBL Heidelberg, , European Molecular Biology Laboratory, ; Heidelberg, Germany
                Author information
                http://orcid.org/0000-0003-3050-1300
                http://orcid.org/0000-0001-8026-3123
                http://orcid.org/0009-0004-4498-3560
                http://orcid.org/0000-0002-5971-1021
                http://orcid.org/0000-0001-9775-0737
                Article
                269
                10.1038/s43705-023-00269-x
                10290634
                37355707
                a34a7b45-8617-4601-af09-72f53a62ff91
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 February 2023
                : 22 May 2023
                : 12 June 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 91951202
                Award ID: 42006134
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 687165
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: 1817354
                Award Recipient :
                Categories
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                © ISME Publications B.V 2023

                microbial ecology,environmental microbiology,next-generation sequencing,metabolism

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