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RNAcentral: a comprehensive database of non-coding RNA sequences

The RNAcentral Consortium 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , *

Nucleic Acids Research

Oxford University Press

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      Abstract

      RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. The website has been subject to continuous improvements focusing on text and sequence similarity searches as well as genome browsing functionality. All RNAcentral data is provided for free and is available for browsing, bulk downloads, and programmatic access at http://rnacentral.org/.

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      Most cited references 46

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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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        An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

        Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ‘taxonomy to tree' approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.
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          Ribosomal Database Project: data and tools for high throughput rRNA analysis

          Ribosomal Database Project (RDP; http://rdp.cme.msu.edu/) provides the research community with aligned and annotated rRNA gene sequence data, along with tools to allow researchers to analyze their own rRNA gene sequences in the RDP framework. RDP data and tools are utilized in fields as diverse as human health, microbial ecology, environmental microbiology, nucleic acid chemistry, taxonomy and phylogenetics. In addition to aligned and annotated collections of bacterial and archaeal small subunit rRNA genes, RDP now includes a collection of fungal large subunit rRNA genes. RDP tools, including Classifier and Aligner, have been updated to work with this new fungal collection. The use of high-throughput sequencing to characterize environmental microbial populations has exploded in the past several years, and as sequence technologies have improved, the sizes of environmental datasets have increased. With release 11, RDP is providing an expanded set of tools to facilitate analysis of high-throughput data, including both single-stranded and paired-end reads. In addition, most tools are now available as open source packages for download and local use by researchers with high-volume needs or who would like to develop custom analysis pipelines.
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            Author and article information

            Affiliations
            [1 ]European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
            [2 ]Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester M13 9PT, UK
            [3 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1HH, UK
            [4 ]Department of Biochemistry, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229-3900, USA
            [5 ]Sandia National Laboratories, Livermore, CA 94551, USA
            [6 ]Department of Biomolecular Engineering, University of California Santa Cruz, CA 95064, USA
            [7 ]Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA
            [8 ]Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, 02-109 Warsaw, Poland
            [9 ]Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Umultowska 89, 61-614 Poznan, Poland
            [10 ]Frontier Science Research Center, University of Miyazaki, Japan
            [11 ]Michigan State University, East Lansing, MI 48824-1325, USA
            [12 ]Department of Computational Biology, Adam Mickiewicz University in Poznan, Poland
            [13 ]Cambridge Systems Biology Centre & Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK
            [14 ]The Arabidopsis Information Resource and Phoenix Bioinformatics, 643 Bair Island Rd. Suite 403, Redwood City, CA 94063, USA
            [15 ]Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
            [16 ]Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
            [17 ]Data Science, National Center for Protein Science, Beijing, China
            [18 ]DIANA-Lab, Department of Electrical & Computer Engineering, University of Thessaly, 382 21 Volos, Greece
            [19 ]Hellenic Pasteur Institute, 127 Vasilissis Sofias Avenue, 11521 Athens, Greece
            [20 ]BIG Data Center and CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
            [21 ]University of Strasbourg, 15 rue R. Descartes, 67084 Strasbourg, France
            [22 ]Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany
            [23 ]Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
            [24 ]dictyBase, Northwestern University, Chicago, IL, USA
            [25 ]Department of Genetics, Stanford University, Stanford, CA 94305, USA
            [26 ]Center for Medical Genetics, Ghent University and Cancer Research Institute Ghent, Ghent University, Ghent, Belgium
            [27 ]Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA
            [28 ]Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
            [29 ]MRC Functional Genomics Unit, Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK
            Author notes
            [* ]To whom correspondence should be addressed. Tel: +44 1223 492550; Fax: +44 1223 494468; Email: apetrov@ 123456ebi.ac.uk
            Journal
            Nucleic Acids Res
            Nucleic Acids Res
            nar
            nar
            Nucleic Acids Research
            Oxford University Press
            0305-1048
            1362-4962
            04 January 2017
            28 October 2016
            28 October 2016
            : 45
            : Database issue , Database issue
            : D128-D134
            27794554 5210518 10.1093/nar/gkw1008
            © The Author(s) 2016. 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/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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            04 January 2017

            Genetics

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