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      Ariadne: a database search engine for identification and chemical analysis of RNA using tandem mass spectrometry data

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          Abstract

          We present here a method to correlate tandem mass spectra of sample RNA nucleolytic fragments with an RNA nucleotide sequence in a DNA/RNA sequence database, thereby allowing tandem mass spectrometry (MS/MS)-based identification of RNA in biological samples. Ariadne, a unique web-based database search engine, identifies RNA by two probability-based evaluation steps of MS/MS data. In the first step, the software evaluates the matches between the masses of product ions generated by MS/MS of an RNase digest of sample RNA and those calculated from a candidate nucleotide sequence in a DNA/RNA sequence database, which then predicts the nucleotide sequences of these RNase fragments. In the second step, the candidate sequences are mapped for all RNA entries in the database, and each entry is scored for a function of occurrences of the candidate sequences to identify a particular RNA. Ariadne can also predict post-transcriptional modifications of RNA, such as methylation of nucleotide bases and/or ribose, by estimating mass shifts from the theoretical mass values. The method was validated with MS/MS data of RNase T1 digests of in vitro transcripts. It was applied successfully to identify an unknown RNA component in a tRNA mixture and to analyze post-transcriptional modification in yeast tRNA Phe-1.

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          Most cited references41

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          The transcriptional landscape of the mammalian genome.

          This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
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            Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

            We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.
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              GtRNAdb: a database of transfer RNA genes detected in genomic sequence

              Transfer RNAs (tRNAs) represent the single largest, best-understood class of non-protein coding RNA genes found in all living organisms. By far, the major source of new tRNAs is computational identification of genes within newly sequenced genomes. To organize the rapidly growing collection and enable systematic analyses, we created the Genomic tRNA Database (GtRNAdb), currently including over 74 000 tRNA genes predicted from 740 species. The web resource provides overview statistics of tRNA genes within each analyzed genome, including information by isotype and genetic locus, easily downloadable primary sequences, graphical secondary structures and multiple sequence alignments. Direct links for each gene to UCSC eukaryotic and microbial genome browsers provide graphical display of tRNA genes in the context of all other local genetic information. The database can be searched by primary sequence similarity, tRNA characteristics or phylogenetic group. The database is publicly available at http://gtrnadb.ucsc.edu.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                April 2009
                6 March 2009
                6 March 2009
                : 37
                : 6
                : e47
                Affiliations
                1Biomolecular Characterization Team, RIKEN Advanced Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, 2Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Sanbancho 5, Chiyoda-ku, Tokyo 102-0075, 3Department of Chemistry, Graduate School of Sciences and Engineering, Tokyo Metropolitan University, Minamiosawa 1-1, Hachioji-shi, Tokyo 192-0397 and 4Department of Biotechnology, United Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Saiwai-cho 3-5-8, Fuchu-shi, Tokyo 183-8509, Japan
                Author notes
                *To whom correspondence should be addressed. Tel: +81 42 677 2542; Fax: +81 42 677 2525; Email: isobe-toshiaki@ 123456tmu.ac.jp
                Article
                gkp099
                10.1093/nar/gkp099
                2665244
                19270066
                90658d55-9058-4602-9eca-cda974945079
                © 2009 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.

                History
                : 26 December 2008
                : 3 February 2009
                : 7 February 2009
                Categories
                Methods Online

                Genetics
                Genetics

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