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rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development

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      Abstract

      Microbiologists utilize ribosomal RNA genes as molecular markers of taxonomy in surveys of microbial communities. rRNA genes are often co-located as part of an rrn operon, and multiple copies of this operon are present in genomes across the microbial tree of life. rrn copy number variability provides valuable insight into microbial life history, but introduces systematic bias when measuring community composition in molecular surveys. Here we present an update to the ribosomal RNA operon copy number database ( rrnDB), a publicly available, curated resource for copy number information for bacteria and archaea. The redesigned rrnDB ( http://rrndb.umms.med.umich.edu/) brings a substantial increase in the number of genomes described, improved curation, mapping of genomes to both NCBI and RDP taxonomies, and refined tools for querying and analyzing these data. With these changes, the rrnDB is better positioned to remain a comprehensive resource under the torrent of microbial genome sequencing. The enhanced rrnDB will contribute to the analysis of molecular surveys and to research linking genomic characteristics to life history.

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      Most cited references 22

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      Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

      The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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        Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

        A 16S rRNA gene database (http://greengenes.lbl.gov) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.
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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            Author and article information

            Affiliations
            [1 ]Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
            [2 ]Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
            [3 ]Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
            [4 ]Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
            Author notes
            [* ]To whom correspondence should be addressed. Tel: +1 734 763 8206; Fax: +1 734 615 5534; Email: schmidti@ 123456umich.edu
            Journal
            Nucleic Acids Res
            Nucleic Acids Res
            nar
            nar
            Nucleic Acids Research
            Oxford University Press
            0305-1048
            1362-4962
            28 January 2015
            20 November 2014
            20 November 2014
            : 43
            : Database issue , Database issue
            : D593-D598
            25414355 4383981 10.1093/nar/gku1201
            © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

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            Pages: 6
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            28 January 2015

            Genetics

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