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      A new genomic taxonomy system for the Synechococcus collective

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          GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database

          Abstract Summary The Genome Taxonomy Database Toolkit (GTDB-Tk) provides objective taxonomic assignments for bacterial and archaeal genomes based on the GTDB. GTDB-Tk is computationally efficient and able to classify thousands of draft genomes in parallel. Here we demonstrate the accuracy of the GTDB-Tk taxonomic assignments by evaluating its performance on a phylogenetically diverse set of 10 156 bacterial and archaeal metagenome-assembled genomes. Availability and implementation GTDB-Tk is implemented in Python and licenced under the GNU General Public Licence v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk. Supplementary information Supplementary data are available at Bioinformatics online.
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            A complete domain-to-species taxonomy for Bacteria and Archaea

            The Genome Taxonomy Database is a phylogenetically consistent, genome-based taxonomy that provides rank-normalized classifications for ~150,000 bacterial and archaeal genomes from domain to genus. However, almost 40% of the genomes in the Genome Taxonomy Database lack a species name. We address this limitation by using commonly accepted average nucleotide identity criteria to set bounds on species and propose species clusters that encompass all publicly available bacterial and archaeal genomes. Unlike previous average nucleotide identity studies, we chose a single representative genome to serve as the effective nomenclatural 'type' defining each species. Of the 24,706 proposed species clusters, 8,792 are based on published names. We assigned placeholder names to the remaining 15,914 species clusters to provide names to the growing number of genomes from uncultivated species. This resource provides a complete domain-to-species taxonomic framework for bacterial and archaeal genomes, which will facilitate research on uncultivated species and improve communication of scientific results.
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              pandas: a foundational Python library for data analysis and statistics.

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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Environmental Microbiology
                Environ Microbiol
                Wiley
                1462-2912
                1462-2920
                November 2020
                August 23 2020
                November 2020
                : 22
                : 11
                : 4557-4570
                Affiliations
                [1 ]Center of Technology‐CT2, SAGE‐COPPE Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil
                [2 ]Department of Systems and Computer Engineering, COPPE Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil
                [3 ]Department of Biomedical Engineering, COPPE Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil
                [4 ]Laboratory of Microbiology Ghent University Ghent Belgium
                [5 ]Institute of Biology Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil
                Article
                10.1111/1462-2920.15173
                32700350
                65d1b68e-c4bc-47b9-a2fa-9b67415f3cc3
                © 2020

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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