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      Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika

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          Abstract

          Lake Tanganyika (LT) is the largest tropical freshwater lake, and the largest body of anoxic freshwater on Earth’s surface. LT’s mixed oxygenated surface waters float atop a permanently anoxic layer and host rich animal biodiversity. However, little is known about microorganisms inhabiting LT’s 1470 meter deep water column and their contributions to nutrient cycling, which affect ecosystem-level function and productivity. Here, we applied genome-resolved metagenomics and environmental analyses to link specific taxa to key biogeochemical processes across a vertical depth gradient in LT. We reconstructed 523 unique metagenome-assembled genomes (MAGs) from 34 bacterial and archaeal phyla, including many rarely observed in freshwater lakes. We identified sharp contrasts in community composition and metabolic potential with an abundance of typical freshwater taxa in oxygenated mixed upper layers, and Archaea and uncultured Candidate Phyla in deep anoxic waters. Genomic capacity for nitrogen and sulfur cycling was abundant in MAGs recovered from anoxic waters, highlighting microbial contributions to the productive surface layers via recycling of upwelled nutrients, and greenhouse gases such as nitrous oxide. Overall, our study provides a blueprint for incorporation of aquatic microbial genomics in the representation of tropical freshwater lakes, especially in the context of ongoing climate change, which is predicted to bring increased stratification and anoxia to freshwater lakes.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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
                karthik@bact.wisc.edu
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                9 February 2021
                9 February 2021
                July 2021
                : 15
                : 7
                : 1971-1986
                Affiliations
                [1 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Bacteriology, , University of Wisconsin–Madison, ; Madison, WI USA
                [2 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Integrative Biology, , University of Wisconsin–Madison, ; Madison, WI USA
                [3 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Natural Resources and the Environment, , Cornell University, ; Ithaca, NY USA
                [4 ]GRID grid.419247.d, ISNI 0000 0001 2108 8097, Department of Ecosystem Research, , Leibniz Institute for Freshwater Ecology and Inland Fisheries, ; Berlin, Germany
                [5 ]GRID grid.268333.f, ISNI 0000 0004 1936 7937, Department of Biological Sciences, , Wright State University, ; Dayton, OH USA
                [6 ]GRID grid.463660.1, Tanzania Fisheries Research Institute (TAFIRI), ; Dar es Salaam, Tanzania
                [7 ]GRID grid.463465.6, ISNI 0000 0004 0648 0690, Ministry of Livestock and Fisheries, ; Dodoma, Tanzania
                [8 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Civil and Environmental Engineering, , University of Wisconsin–Madison, ; Madison, WI USA
                Author information
                http://orcid.org/0000-0003-3948-3938
                http://orcid.org/0000-0002-3390-9005
                http://orcid.org/0000-0002-1101-5262
                http://orcid.org/0000-0002-9584-2491
                Article
                898
                10.1038/s41396-021-00898-x
                8245535
                33564113
                f2a0eec6-305c-4647-8087-652042cbe9c7
                © The Author(s) 2021

                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
                : 23 July 2020
                : 22 December 2020
                : 18 January 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000038, Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada);
                Award ID: CGSD3 - 532474 - 2019
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000155, NSF | BIO | Division of Environmental Biology (DEB);
                Award ID: DEB-1030242
                Award ID: DEB-0842253
                Award ID: DEB-1344254
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                biogeochemistry,limnology,microbial ecology,freshwater ecology
                Microbiology & Virology
                biogeochemistry, limnology, microbial ecology, freshwater ecology

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