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RNAmmer: consistent and rapid annotation of ribosomal RNA genes

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      Abstract

      The publication of a complete genome sequence is usually accompanied by annotations of its genes. In contrast to protein coding genes, genes for ribosomal RNA (rRNA) are often poorly or inconsistently annotated. This makes comparative studies based on rRNA genes difficult. We have therefore created computational predictors for the major rRNA species from all kingdoms of life and compiled them into a program called RNAmmer. The program uses hidden Markov models trained on data from the 5S ribosomal RNA database and the European ribosomal RNA database project. A pre-screening step makes the method fast with little loss of sensitivity, enabling the analysis of a complete bacterial genome in less than a minute. Results from running RNAmmer on a large set of genomes indicate that the location of rRNAs can be predicted with a very high level of accuracy. Novel, unannotated rRNAs are also predicted in many genomes. The software as well as the genome analysis results are available at the CBS web server.

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      Most cited references 26

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      Basic local alignment search tool.

      A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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        tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence.

        We describe a program, tRNAscan-SE, which identifies 99-100% of transfer RNA genes in DNA sequence while giving less than one false positive per 15 gigabases. Two previously described tRNA detection programs are used as fast, first-pass prefilters to identify candidate tRNAs, which are then analyzed by a highly selective tRNA covariance model. This work represents a practical application of RNA covariance models, which are general, probabilistic secondary structure profiles based on stochastic context-free grammars. tRNAscan-SE searches at approximately 30 000 bp/s. Additional extensions to tRNAscan-SE detect unusual tRNA homologues such as selenocysteine tRNAs, tRNA-derived repetitive elements and tRNA pseudogenes.
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          Profile hidden Markov models.

           Sean R. Eddy,  S Eddy (1997)
          The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
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            Author and article information

            Affiliations
            1Centre for Molecular Biology and Neuroscience and Institute of Medical Microbiology, University of Oslo, NO-0027 Oslo, Norway, 2Centre for Molecular Biology and Neuroscience and Institute of Medical Microbiology, Rikshospitalet-Radiumhospitalet Medical Centre, NO-0027 Oslo, Norway, 3Center for Biological Sequence Analysis, Biocentrum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark, 4Department of Informatics, University of Oslo, PO Box 1080 Blindern, NO-0316 Oslo, Norway and 5Norwegian Computing Center, PO Box 114 Blindern, NO-0314 Oslo, Norway
            Author notes
            *To whom correspondence should be addressed. +4722844786 karin.lagesen@ 123456medisin.uio.no
            Journal
            Nucleic Acids Res
            Nucleic Acids Res
            nar
            Nucleic Acids Research
            Nucleic Acids Research
            Oxford University Press
            0305-1048
            1362-4962
            May 2007
            22 April 2007
            22 April 2007
            : 35
            : 9
            : 3100-3108
            17452365
            1888812
            10.1093/nar/gkm160
            © 2007 The Author(s)

            This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Computational Biology

            Genetics

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