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      Species abundance information improves sequence taxonomy classification accuracy

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          Abstract

          Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

          Abstract

          Taxonomy classification of amplicon sequences is an important step in investigating microbial communities in microbiome analysis. Here, the authors show incorporating environment-specific taxonomic abundance information can lead to improved species-level classification accuracy across common sample types.

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          The Soil Microbiome Influences Grapevine-Associated Microbiota

          ABSTRACT Grapevine is a well-studied, economically relevant crop, whose associated bacteria could influence its organoleptic properties. In this study, the spatial and temporal dynamics of the bacterial communities associated with grapevine organs (leaves, flowers, grapes, and roots) and soils were characterized over two growing seasons to determine the influence of vine cultivar, edaphic parameters, vine developmental stage (dormancy, flowering, preharvest), and vineyard. Belowground bacterial communities differed significantly from those aboveground, and yet the communities associated with leaves, flowers, and grapes shared a greater proportion of taxa with soil communities than with each other, suggesting that soil may serve as a bacterial reservoir. A subset of soil microorganisms, including root colonizers significantly enriched in plant growth-promoting bacteria and related functional genes, were selected by the grapevine. In addition to plant selective pressure, the structure of soil and root microbiota was significantly influenced by soil pH and C:N ratio, and changes in leaf- and grape-associated microbiota were correlated with soil carbon and showed interannual variation even at small spatial scales. Diazotrophic bacteria, e.g., Rhizobiaceae and Bradyrhizobium spp., were significantly more abundant in soil samples and root samples of specific vineyards. Vine-associated microbial assemblages were influenced by myriad factors that shape their composition and structure, but the majority of organ-associated taxa originated in the soil, and their distribution reflected the influence of highly localized biogeographic factors and vineyard management.
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            Advancing our understanding of the human microbiome using QIIME.

            High-throughput DNA sequencing technologies, coupled with advanced bioinformatics tools, have enabled rapid advances in microbial ecology and our understanding of the human microbiome. QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics software package designed for microbial community analysis based on DNA sequence data, which provides a single analysis framework for analysis of raw sequence data through publication-quality statistical analyses and interactive visualizations. In this chapter, we demonstrate the use of the QIIME pipeline to analyze microbial communities obtained from several sites on the bodies of transgenic and wild-type mice, as assessed using 16S rRNA gene sequences generated on the Illumina MiSeq platform. We present our recommended pipeline for performing microbial community analysis and provide guidelines for making critical choices in the process. We present examples of some of the types of analyses that are enabled by QIIME and discuss how other tools, such as phyloseq and R, can be applied to expand upon these analyses. © 2013 Elsevier Inc. All rights reserved.
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              What Makes a Bacterial Species Pathogenic?:Comparative Genomic Analysis of the Genus Leptospira

              Leptospirosis, caused by spirochetes of the genus Leptospira, is a globally widespread, neglected and emerging zoonotic disease. While whole genome analysis of individual pathogenic, intermediately pathogenic and saprophytic Leptospira species has been reported, comprehensive cross-species genomic comparison of all known species of infectious and non-infectious Leptospira, with the goal of identifying genes related to pathogenesis and mammalian host adaptation, remains a key gap in the field. Infectious Leptospira, comprised of pathogenic and intermediately pathogenic Leptospira, evolutionarily diverged from non-infectious, saprophytic Leptospira, as demonstrated by the following computational biology analyses: 1) the definitive taxonomy and evolutionary relatedness among all known Leptospira species; 2) genomically-predicted metabolic reconstructions that indicate novel adaptation of infectious Leptospira to mammals, including sialic acid biosynthesis, pathogen-specific porphyrin metabolism and the first-time demonstration of cobalamin (B12) autotrophy as a bacterial virulence factor; 3) CRISPR/Cas systems demonstrated only to be present in pathogenic Leptospira, suggesting a potential mechanism for this clade’s refractoriness to gene targeting; 4) finding Leptospira pathogen-specific specialized protein secretion systems; 5) novel virulence-related genes/gene families such as the Virulence Modifying (VM) (PF07598 paralogs) proteins and pathogen-specific adhesins; 6) discovery of novel, pathogen-specific protein modification and secretion mechanisms including unique lipoprotein signal peptide motifs, Sec-independent twin arginine protein secretion motifs, and the absence of certain canonical signal recognition particle proteins from all Leptospira; and 7) and demonstration of infectious Leptospira-specific signal-responsive gene expression, motility and chemotaxis systems. By identifying large scale changes in infectious (pathogenic and intermediately pathogenic) vs. non-infectious Leptospira, this work provides new insights into the evolution of a genus of bacterial pathogens. This work will be a comprehensive roadmap for understanding leptospirosis pathogenesis. More generally, it provides new insights into mechanisms by which bacterial pathogens adapt to mammalian hosts.
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                Author and article information

                Contributors
                b.kaehler@adfa.edu.au
                nicholas.bokulich@nau.edu
                gregcaporaso@gmail.com
                gavin.huttley@anu.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 October 2019
                11 October 2019
                2019
                : 10
                : 4643
                Affiliations
                [1 ]ISNI 0000 0001 2180 7477, GRID grid.1001.0, Research School of Biology, , Australian National University, ; Canberra, Australia
                [2 ]ISNI 0000 0004 4902 0432, GRID grid.1005.4, School of Science, , University of New South Wales, ; Canberra, Australia
                [3 ]ISNI 0000 0004 1936 8040, GRID grid.261120.6, Center for Applied Microbiome Science, The Pathogen and Microbiome Institute, , Northern Arizona University, ; Flagstaff, AZ USA
                [4 ]ISNI 0000 0004 1936 8040, GRID grid.261120.6, Department of Biological Sciences, , Northern Arizona University, ; Flagstaff, AZ USA
                [5 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Department of Pediatrics, , University of California San Diego, ; La Jolla, CA USA
                [6 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Department of Computer Science and Engineering, , University of California San Diego, ; La Jolla, CA USA
                [7 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Center for Microbiome Innovation, , University of California San Diego, ; La Jolla, CA USA
                Author information
                http://orcid.org/0000-0002-5318-9551
                http://orcid.org/0000-0002-1784-8935
                http://orcid.org/0000-0002-0975-9019
                http://orcid.org/0000-0002-8865-1670
                http://orcid.org/0000-0001-7224-2074
                Article
                12669
                10.1038/s41467-019-12669-6
                6789115
                31604942
                074e1d93-6d1f-4104-897f-9470229afac5
                © The Author(s) 2019

                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
                : 27 February 2019
                : 19 September 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: APP1085372
                Award ID: APP1085372
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                classification and taxonomy,computational platforms and environments,statistical methods,microbiome

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