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      Biosynthetic potential of the global ocean microbiome

      research-article
      1 , 1 , 2 , 2 , 3 , 2 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 4 , 5 , 6 , 4 , 5 , 6 , 7 , 7 , 8 , 9 , 9 , 8 , 7 , 10 , 11 , 12 , 13 , 13 , 14 , 8 , 15 , 4 , 5 , 6 , 8 , 16 , 13 , 14 , 7 , 2 , 17 , , 2 , , 1 ,
      Nature
      Nature Publishing Group UK
      Computational biology and bioinformatics, Environmental microbiology

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          Abstract

          Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups 1 , this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds 2, 3 . However, studying this diversity to identify genomic pathways for the synthesis of such compounds 4 and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters (‘ Candidatus Eudoremicrobiaceae’) that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments.

          Abstract

          Global ocean microbiome survey reveals the bacterial family ‘ Candidatus Eudoremicrobiaceae’, which includes some of the most biosynthetically diverse microorganisms in the ocean environment.

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          Most cited references108

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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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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

                Contributors
                Serina.Robinson@eawag.ch
                jpiel@ethz.ch
                ssunagawa@ethz.ch
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                22 June 2022
                22 June 2022
                2022
                : 607
                : 7917
                : 111-118
                Affiliations
                [1 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, , ETH Zurich, ; Zurich, Switzerland
                [2 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Department of Biology, Institute of Microbiology, , ETH Zurich, ; Zurich, Switzerland
                [3 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Bioinformatics Group, , Wageningen University, ; Wageningen, The Netherlands
                [4 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Department of Computer Science, , ETH Zurich, ; Zurich, Switzerland
                [5 ]GRID grid.412004.3, ISNI 0000 0004 0478 9977, Biomedical Informatics Research, , University Hospital Zurich, ; Zurich, Switzerland
                [6 ]GRID grid.419765.8, ISNI 0000 0001 2223 3006, Swiss Institute of Bioinformatics, ; Lausanne, Switzerland
                [7 ]GRID grid.4709.a, ISNI 0000 0004 0495 846X, Structural and Computational Biology Unit, , European Molecular Biology Laboratory, ; Heidelberg, Germany
                [8 ]GRID grid.428945.6, Department of Marine Biology and Oceanography, , Institute of Marine Sciences ICM-CSIC, ; Barcelona, Spain
                [9 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Center of Microbiome Science, EMERGE Biology Integration Institute, Department of Microbiology, , The Ohio State University, ; Columbus, OH USA
                [10 ]GRID grid.419491.0, ISNI 0000 0001 1014 0849, Max Delbrück Centre for Molecular Medicine, ; Berlin, Germany
                [11 ]GRID grid.8379.5, ISNI 0000 0001 1958 8658, Department of Bioinformatics, Biocenter, , University of Würzburg, ; Würzburg, Germany
                [12 ]GRID grid.440907.e, ISNI 0000 0004 1784 3645, Institut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, , Université PSL, ; Paris, France
                [13 ]Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE, Paris, France
                [14 ]GRID grid.8390.2, ISNI 0000 0001 2180 5818, Metabolic Genomics, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, , Univ Evry, Université Paris Saclay, ; Evry, France
                [15 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Department of Biology, Biomolecular NMR Spectroscopy Platform, , ETH Zurich, ; Zurich, Switzerland
                [16 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Department of Civil, Environmental and Geodetic Engineering, , The Ohio State University, ; Columbus, OH USA
                [17 ]GRID grid.418656.8, ISNI 0000 0001 1551 0562, Department of Environmental Microbiology, , Swiss Federal Institute of Aquatic Science and Technology (Eawag), ; Dübendorf, Switzerland
                Author information
                http://orcid.org/0000-0003-0771-8309
                http://orcid.org/0000-0001-7473-6086
                http://orcid.org/0000-0002-9786-1493
                http://orcid.org/0000-0002-7050-2239
                http://orcid.org/0000-0002-3909-7378
                http://orcid.org/0000-0001-6200-5972
                http://orcid.org/0000-0002-3947-4444
                http://orcid.org/0000-0003-2787-822X
                http://orcid.org/0000-0003-2793-2679
                http://orcid.org/0000-0002-3439-0428
                http://orcid.org/0000-0002-2627-833X
                http://orcid.org/0000-0003-3835-6187
                http://orcid.org/0000-0001-7053-7848
                http://orcid.org/0000-0001-5238-2387
                http://orcid.org/0000-0001-7732-495X
                http://orcid.org/0000-0002-3411-0692
                http://orcid.org/0000-0001-8398-8234
                http://orcid.org/0000-0003-1429-7485
                http://orcid.org/0000-0001-6947-7913
                http://orcid.org/0000-0002-2282-8154
                http://orcid.org/0000-0003-3065-0314
                Article
                4862
                10.1038/s41586-022-04862-3
                9259500
                35732736
                b11392de-c5e7-435f-ab90-a5d885bc80d5
                © The Author(s) 2022

                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
                : 21 May 2021
                : 12 May 2022
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                © The Author(s), under exclusive licence to Springer Nature Limited 2022

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                computational biology and bioinformatics,environmental microbiology
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                computational biology and bioinformatics, environmental microbiology

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