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The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs

, 1 , *

Nucleic Acids Research

Oxford University Press

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      Abstract

      Transfer RNAs (tRNAs) and small nucleolar RNAs (snoRNAs) are two of the largest classes of non-protein-coding RNAs. Conventional gene finders that detect protein-coding genes do not find tRNA and snoRNA genes because they lack the codon structure and statistical signatures of protein-coding genes. Previously, we developed tRNAscan-SE, snoscan and snoGPS for the detection of tRNAs, methylation-guide snoRNAs and pseudouridylation-guide snoRNAs, respectively. tRNAscan-SE is routinely applied to completed genomes, resulting in the identification of thousands of tRNA genes. Snoscan has successfully detected methylation-guide snoRNAs in a variety of eukaryotes and archaea, and snoGPS has identified novel pseudouridylation-guide snoRNAs in yeast and mammals. Although these programs have been quite successful at RNA gene detection, their use has been limited by the need to install and configure the software packages on UNIX workstations. Here, we describe online implementations of these RNA detection tools that make these programs accessible to a wider range of research biologists. The tRNAscan-SE, snoscan and snoGPS servers are available at http://lowelab.ucsc.edu/tRNAscan-SE/, http://lowelab.ucsc.edu/snoscan/ and http://lowelab.ucsc.edu/snoGPS/, respectively.

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      Most cited references 17

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        RNA sequence analysis using covariance models.

        We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases. A model can be built automatically from an existing sequence alignment. We also describe an algorithm for learning a model and hence a consensus secondary structure from initially unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA secondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences.
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          A computational screen for methylation guide snoRNAs in yeast.

           Emma Lowe,  S Eddy (1999)
          Small nucleolar RNAs (snoRNAs) are required for ribose 2'-O-methylation of eukaryotic ribosomal RNA. Many of the genes for this snoRNA family have remained unidentified in Saccharomyces cerevisiae, despite the availability of a complete genome sequence. Probabilistic modeling methods akin to those used in speech recognition and computational linguistics were used to computationally screen the yeast genome and identify 22 methylation guide snoRNAs, snR50 to snR71. Gene disruptions and other experimental characterization confirmed their methylation guide function. In total, 51 of the 55 ribose methylated sites in yeast ribosomal RNA were assigned to 41 different guide snoRNAs.
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            Author and article information

            Affiliations
            simpleDepartment of Biomolecular Engineering and the UCSC RNA Center, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
            1simpleDivision of Biological Sciences, Cell and Developmental Biology Section and Center for Molecular Genetics, University of California at San Diego La Jolla, CA 92093, USA
            Author notes
            *To whom correspondence should be addressed. Tel: +1 831 459 1511; Fax: +1 831 459 3139; Email: lowe@ 123456soe.ucsc.edu

            Correspondence may also be addressed to Peter Schattner. Email: schattner@ 123456soe.ucsc.edu

            Journal
            Nucleic Acids Res
            Nucleic Acids Research
            Nucleic Acids Research
            Oxford University Press
            0305-1048
            1362-4962
            01 July 2005
            01 July 2005
            27 June 2005
            : 33
            : Web Server issue
            : W686-W689
            1160127
            10.1093/nar/gki366
            15980563
            © The Author 2005. Published by Oxford University Press. All rights reserved

            The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@ 123456oupjournals.org

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            Genetics

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