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      Expanding the genomic encyclopedia of Actinobacteria with 824 isolate reference genomes

      research-article
      1 , 13 , , 1 , 2 , 1 , 3 , 1 , 3 , 1 , 1 , 2 , 2 , 4 , 1 , 1 , 1 , 2 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 3 , 1 , 5 , 6 , 7 , 7 , 1 , 3 , 1 , 3 , 1 , 3 , 10 , 11 , 12 , 8 , 9 , 1 , 3 , 2 , ∗∗ , 1 , 3 , 1 , 3 , ∗∗∗
      Cell Genomics
      Elsevier
      microbiology, comparative genomics, secondary metabolites, actinobacteria, metagenomics, ecology, evolution, mycobacteria

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          Summary

          The phylum Actinobacteria includes important human pathogens like Mycobacterium tuberculosis and Corynebacterium diphtheriae and renowned producers of secondary metabolites of commercial interest, yet only a small part of its diversity is represented by sequenced genomes. Here, we present 824 actinobacterial isolate genomes in the context of a phylum-wide analysis of 6,700 genomes including public isolates and metagenome-assembled genomes (MAGs). We estimate that only 30%–50% of projected actinobacterial phylogenetic diversity possesses genomic representation via isolates and MAGs. A comparison of gene functions reveals novel determinants of host-microbe interaction as well as environment-specific adaptations such as potential antimicrobial peptides. We identify plasmids and prophages across isolates and uncover extensive prophage diversity structured mainly by host taxonomy. Analysis of >80,000 biosynthetic gene clusters reveals that horizontal gene transfer and gene loss shape secondary metabolite repertoire across taxa. Our observations illustrate the essential role of and need for high-quality isolate genome sequences.

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          Highlights

          • 824 new actinobacterial isolate genomes from diverse environments

          • Only a third of actinobacterial diversity has genome representation

          • New niche-specific gene determinants highlighted, such as new antimicrobial peptides

          • Secondary metabolite gene clusters shaped by horizontal gene transfer

          Abstract

          Seshadri et al. contribute 824 new genomes of cultivated Actinobacteria, which are important for drug discovery. They observe that the genes responsible for producing such compounds often move around between microbes, making them harder to capture without high-quality genomes. They highlight interesting adaptations such as an experimentally verified antimicrobial peptide.

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          Most cited references107

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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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            Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation

            The Interactive Tree Of Life ( https://itol.embl.de ) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 5 introduces a completely new tree display engine, together with numerous new features. For example, a new dataset type has been added (MEME motifs), while annotation options have been expanded for several existing ones. Node metadata display options have been extended and now also support non-numerical categorical values, as well as multiple values per node. Direct manual annotation is now available, providing a set of basic drawing and labeling tools, allowing users to draw shapes, labels and other features by hand directly onto the trees. Support for tree and dataset scales has been extended, providing fine control over line and label styles. Unrooted tree displays can now use the equal-daylight algorithm, proving a much greater display clarity. The user account system has been streamlined and expanded with new navigation options and currently handles >1 million trees from >70 000 individual users. Graphical Abstract iTOL: an online tool for the tree display and annotation.
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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
                Journal
                Cell Genom
                Cell Genom
                Cell Genomics
                Elsevier
                2666-979X
                11 November 2022
                14 December 2022
                11 November 2022
                : 2
                : 12
                : 100213
                Affiliations
                [1 ]US Department of Energy Joint Genome Institute, Berkeley, CA, USA
                [2 ]Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
                [3 ]Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
                [4 ]Resphera Biosciences, Baltimore, MD, USA
                [5 ]China General Microbiological Culture Collection Center, Beijing, China
                [6 ]Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences, All-Russian Collection of Microorganisms (VKM), Pushchino, Russia
                [7 ]Center for Environmental Sciences, Environmental Biology, Hasselt University, Diepenbeek, Belgium
                [8 ]Department of Microbiology, University of Georgia, Athens, GA, USA
                [9 ]School of Biology, Newcastle University, Newcastle upon Tyne, UK
                [10 ]Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
                [11 ]Center for Advanced Bioenergy and Bioproducts Innovation, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
                [12 ]Global Institution for Collaborative Research and Education, Hokkaido University, Hokkaido 060-8589, Japan
                Author notes
                []Corresponding author rseshadri@ 123456lbl.gov
                [∗∗ ]Corresponding author markus.goeker@ 123456dsmz.de
                [∗∗∗ ]Corresponding author nnivanova@ 123456lbl.gov
                [13]

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                Article
                S2666-979X(22)00166-5 100213
                10.1016/j.xgen.2022.100213
                9903846
                36778052
                e0ec45c5-cf83-4192-9fda-940f7f5c95e9
                © 2022 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 January 2022
                : 19 July 2022
                : 16 October 2022
                Categories
                Article

                microbiology,comparative genomics,secondary metabolites,actinobacteria,metagenomics,ecology,evolution,mycobacteria

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