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      RNA-sequencing of a mouse-model of spinal muscular atrophy reveals tissue-wide changes in splicing of U12-dependent introns

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          Abstract

          Spinal Muscular Atrophy (SMA) is a neuromuscular disorder caused by insufficient levels of the Survival of Motor Neuron (SMN) protein. SMN is expressed ubiquitously and functions in RNA processing pathways that include trafficking of mRNA and assembly of snRNP complexes. Importantly, SMA severity is correlated with decreased snRNP assembly activity. In particular, the minor spliceosomal snRNPs are affected, and some U12-dependent introns have been reported to be aberrantly spliced in patient cells and animal models. SMA is characterized by loss of motor neurons, but the underlying mechanism is largely unknown. It is likely that aberrant splicing of genes expressed in motor neurons is involved in SMA pathogenesis, but increasing evidence indicates that pathologies also exist in other tissues. We present here a comprehensive RNA-seq study that covers multiple tissues in an SMA mouse model. We show elevated U12-intron retention in all examined tissues from SMA mice, and that U12-dependent intron retention is induced upon siRNA knock-down of SMN in HeLa cells. Furthermore, we show that retention of U12-dependent introns is mitigated by ASO treatment of SMA mice and that many transcriptional changes are reversed. Finally, we report on missplicing of several Ca 2+ channel genes that may explain disrupted Ca 2+ homeostasis in SMA and activation of Cdk5.

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          Most cited references67

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          Pathview: an R/Bioconductor package for pathway-based data integration and visualization

          Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. Contact: luo_weijun@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.
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            NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

            The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
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              Identification of novel transcripts in annotated genomes using RNA-Seq.

              We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. cole@broadinstitute.org; lpachter@math.berkeley.edu Supplementary data are available at Bioinformatics online.
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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
                09 January 2017
                23 August 2016
                23 August 2016
                : 45
                : 1
                : 395-416
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
                [2 ]The Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense, Denmark
                [3 ]Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
                [4 ]Department of Gamete and Embryo Biology, Division of Reproductive Biology, Institute of Animal Reproduction and Food Research of Polish Academy of Sciences, 10-243 Olsztyn, Poland
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +45 65502413; Fax: +45 65502467; Email: bragea@ 123456bmb.sdu.dk
                Article
                10.1093/nar/gkw731
                5224493
                27557711
                454ea8b3-493e-4809-b334-a068a040d54a
                © 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-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 10 August 2016
                : 09 August 2016
                : 08 July 2016
                Page count
                Pages: 22
                Categories
                RNA
                Custom metadata
                09 January 2017

                Genetics
                Genetics

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