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      U7 snRNAs: A Computational Survey

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          Abstract

          U7 small nuclear RNA (snRNA) sequences have been described only for a handful of animal species in the past. Here we describe a computational search for functional U7 snRNA genes throughout vertebrates including the upstream sequence elements characteristic for snRNAs transcribed by polymerase II. Based on the results of this search, we discuss the high variability of U7 snRNAs in both sequence and structure, and report on an attempt to find U7 snRNA sequences in basal deuterostomes and non-drosophilids insect genomes based on a combination of sequence, structure, and promoter features. Due to the extremely short sequence and the high variability in both sequence and structure, no unambiguous candidates were found. These results cast doubt on putative U7 homologs in even more distant organisms that are reported in the most recent release of the Rfam database.

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          Most cited references 40

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          CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

          The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.
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            WebLogo: a sequence logo generator.

            WebLogo generates sequence logos, graphical representations of the patterns within a multiple sequence alignment. Sequence logos provide a richer and more precise description of sequence similarity than consensus sequences and can rapidly reveal significant features of the alignment otherwise difficult to perceive. Each logo consists of stacks of letters, one stack for each position in the sequence. The overall height of each stack indicates the sequence conservation at that position (measured in bits), whereas the height of symbols within the stack reflects the relative frequency of the corresponding amino or nucleic acid at that position. WebLogo has been enhanced recently with additional features and options, to provide a convenient and highly configurable sequence logo generator. A command line interface and the complete, open WebLogo source code are available for local installation and customization. Copyright 2004 Cold Spring Harbor Laboratory Press
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              Rfam: annotating non-coding RNAs in complete genomes

              Rfam is a comprehensive collection of non-coding RNA (ncRNA) families, represented by multiple sequence alignments and profile stochastic context-free grammars. Rfam aims to facilitate the identification and classification of new members of known sequence families, and distributes annotation of ncRNAs in over 200 complete genome sequences. The data provide the first glimpses of conservation of multiple ncRNA families across a wide taxonomic range. A small number of large families are essential in all three kingdoms of life, with large numbers of smaller families specific to certain taxa. Recent improvements in the database are discussed, together with challenges for the future. Rfam is available on the Web at http://www.sanger.ac.uk/Software/Rfam/ and http://rfam.wustl.edu/.
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                Author and article information

                Contributors
                Journal
                Genomics Proteomics Bioinformatics
                Genomics Proteomics Bioinformatics
                Genomics, Proteomics & Bioinformatics
                Elsevier
                1672-0229
                2210-3244
                08 February 2008
                2007
                08 February 2008
                : 5
                : 3-4
                : 187-195
                Affiliations
                [1 ]Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig D-04107, Germany
                [2 ]Department of Combinatorics and Geometry, CAS/MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
                [3 ]Max Planck Institute for Mathematics in the Sciences, Leipzig D-04103, Germany
                [4 ]Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig D-04107, Germany
                [5 ]Fraunhofer Institute for Cell Therapy and Immunology, Leipzig D-04103, Germany
                [6 ]Department of Theoretical Chemistry, University of Vienna, Vienna A-1090, Austria
                [7 ]Santa Fe Institute, Santa Fe, NM 87501, USA
                Author notes
                [* ]Corresponding author. studla@ 123456bioinf.uni-leipzig.de
                Article
                S1672-0229(08)60006-6
                10.1016/S1672-0229(08)60006-6
                5054213
                18267300
                © 2007 Beijing Institute of Genomics

                This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).

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