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      Alternative splicing regulation of membrane trafficking genes during myogenesis

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          Abstract

          Alternative splicing transitions occur during organ development, and, in numerous diseases, splicing programs revert to fetal isoform expression. We previously found that extensive splicing changes occur during postnatal mouse heart development in genes encoding proteins involved in vesicle-mediated trafficking. However, the regulatory mechanisms of this splicing-trafficking network are unknown. Here, we found that membrane trafficking genes are alternatively spliced in a tissue-specific manner, with striated muscles exhibiting the highest levels of alternative exon inclusion. Treatment of differentiated muscle cells with chromatin-modifying drugs altered exon inclusion in muscle cells. Examination of several RNA-binding proteins revealed that the poly-pyrimidine tract binding protein 1 (PTBP1) and quaking regulate splicing of trafficking genes during myogenesis, and that removal of PTBP1 motifs prevented PTBP1 from binding its RNA target. These findings enhance our understanding of developmental splicing regulation of membrane trafficking proteins which might have implications for muscle disease pathogenesis.

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          Most cited references78

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                RNA
                RNA
                RNA
                RNA
                Cold Spring Harbor Laboratory Press
                1355-8382
                1469-9001
                April 2022
                : 28
                : 4
                : 523-540
                Affiliations
                [1 ]Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
                [2 ]Curriculum in Genetics and Molecular Biology (GMB), The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
                [3 ]Department of Biochemistry and Biophysics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
                [4 ]Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
                [5 ]Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
                [6 ]McAllister Heart Institute, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
                Author notes
                [7]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-7396-7525
                http://orcid.org/0000-0002-5919-5944
                http://orcid.org/0000-0002-1951-7320
                http://orcid.org/0000-0002-5205-3513
                http://orcid.org/0000-0002-9774-6778
                http://orcid.org/0000-0002-2669-1861
                http://orcid.org/0000-0002-3330-7784
                Article
                9509184 RA
                10.1261/rna.078993.121
                8925968
                35082143
                caab8c17-10fc-4827-84c2-e7af8ecd1ebc
                © 2022 Hinkle et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society

                This article is distributed exclusively by the RNA Society for the first 12 months after the full-issue publication date (see http://rnajournal.cshlp.org/site/misc/terms.xhtml). After 12 months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 27 September 2021
                : 17 December 2021
                Page count
                Pages: 35
                Funding
                Funded by: start-up funds
                Funded by: Jefferson Pilot Award
                Funded by: The University of North Carolina at Chapel Hill, the National Institutes of Health
                Award ID: NIH-NIGMS R01GM130866
                Funded by: a Career Development Award from the American Heart Association
                Award ID: 19CDA34660248
                Funded by: NIH-NIAMS F31 predoctoral fellowship
                Award ID: AR077381-01A1
                Funded by: NIH-NIGMS training award
                Award ID: 5T32GM007092
                Funded by: merit-doctoral fellowship from the Graduate School at The University of North Carolina at Chapel Hill
                Funded by: a NIH-NIGMS training award
                Award ID: T32GM119999
                Funded by: the NSF Graduate Research Fellowship Program
                Award ID: DGE-1650116
                Funded by: NIH-NIGMS training award
                Award ID: T32GM119999
                Funded by: by the NSF Graduate Research Fellowship Program
                Award ID: DGE-1650116
                Funded by: Chancellor's Science Scholars Program at the University of North Carolina at Chapel Hill
                Funded by: NIH-NHLBI F32 postdoctoral fellowship
                Award ID: F32HL149147
                Funded by: NIH-NIGMS training award
                Award ID: T32GM008570
                Funded by: NIH-NIGMS training awards
                Award ID: T32GM135095
                Award ID: R25GM055336
                Funded by: Genetics and Molecular Biology Curriculum (GMB), the Program in Translational Medicine, and the Mechanistic and Interdisciplinary Biology (MiBio) Graduate Training Program, all at The University of North Carolina at Chapel Hill
                Categories
                Article

                alternative splicing,myogenesis,rna-binding proteins,membrane trafficking

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