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      Not only cancer: the long non-coding RNA MALAT1 affects the repertoire of alternatively spliced transcripts and circular RNAs in multiple sclerosis

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          Abstract

          Long non-coding RNAs (lncRNAs) are post-transcriptional and epigenetic regulators, whose implication in neurodegenerative and autoimmune diseases remains poorly understood. We analyzed publicly available microarray data sets to identify dysregulated lncRNAs in multiple sclerosis (MS), a neuroinflammatory autoimmune disease. We found a consistent upregulation in MS of the lncRNA MALAT1 (2.7-fold increase; meta-analysis, P = 1.3 × 10−8; 190 cases, 182 controls), known to regulate alternative splicing (AS). We confirmed MALAT1 upregulation in two independent MS cohorts (1.5-fold increase; P < 0.01; 59 cases, 50 controls). We hence performed MALAT1 overexpression/knockdown in cell lines, demonstrating that its modulation impacts on endogenous expression of splicing factors (HNRNPF and HNRNPH1) and on AS of MS-associated genes (IL7R and SP140). Minigene-based splicing assays upon MALAT1 modulation recapitulated IL7R and SP140 isoform unbalances observed in patients. RNA-sequencing of MALAT1-knockdown Jurkat cells further highlighted MALAT1 role in splicing (approximately 1100 significantly-modulated AS events) and revealed its contribution to backsplicing (approximately 50 differentially expressed circular RNAs). Our study proposes a possible novel role for MALAT1 dysregulation and the consequent AS alteration in MS pathogenesis, based on anomalous splicing/backsplicing profiles of MS-relevant genes.

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          Most cited references96

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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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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Is Open Access

              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Journal
                Human Molecular Genetics
                Oxford University Press (OUP)
                0964-6906
                1460-2083
                May 01 2019
                May 01 2019
                December 19 2018
                May 01 2019
                May 01 2019
                December 19 2018
                : 28
                : 9
                : 1414-1428
                Affiliations
                [1 ]Department of Biomedical Sciences, Humanitas University, Pieve Emanuele Milan, Italy
                [2 ]Humanitas Clinical and Research Center, Rozzano Milan, Italy
                [3 ]Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
                Article
                10.1093/hmg/ddy438
                30566690
                c958a941-019e-4339-a06a-c96a0f783f1e
                © 2018

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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