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      Long noncoding RNA U90926 is crucial for herpes simplex virus type 1 proliferation in murine retinal photoreceptor cells

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          Abstract

          Long non-coding RNAs (lncRNAs) play vital roles in the pathogenesis of infectious diseases, but the role of lncRNAs in herpes simplex virus 1 (HSV-1) infection remains unknown. Using RNA sequencing analysis, we explored lncRNAs that were highly expressed in murine retinal photoreceptor cell-derived 661W cells infected with HSV-1. U90926 RNA (522 nucleotides) was the most upregulated lncRNA detected post HSV-1 infection. The level of U90926 RNA was continuously increased post HSV-1 infection, reaching a 100-fold increase at 24 h. Cellular fractionation showed that U90926 RNA was located in the nucleus post HSV-1 infection. Downregulation of U90926 expression by RNA interference markedly suppressed HSV-1 DNA replication (80% reduction at 12 h post infection) and HSV-1 proliferation (93% reduction at 12 h post infection) in 661W cells. The survival rates of U90926-knockdown cells were significantly increased compared to those of control cells (81% and 21%, respectively; p < 0.0001). Thus, lncRNA U90926 is crucial for HSV-1 proliferation in retinal photoreceptor cells and consequently leads to host cell death by promoting HSV-1 proliferation.

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          Most cited references33

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          HISAT: a fast spliced aligner with low memory requirements.

          HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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            StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

            Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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              Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

              High-throughput sequencing of messenger RNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate, and flexible software to reduce the raw read data to comprehensible results. HISAT, StringTie, and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample, and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol’s execution time depends on the computing resources, but typically takes under 45 minutes of computer time. Pertea et al. describe a protocol to analyze RNA-seq data using HISAT, StringTie, and Ballgown (the “new Tuxedo” package). The protocol can be used for assembly of transcripts, quantification of gene expression levels and differential expression analysis.
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                Author and article information

                Contributors
                akimitsu@ric.u-tokyo.ac.jp
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 November 2020
                10 November 2020
                2020
                : 10
                : 19406
                Affiliations
                [1 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Department of Ophthalmology, Graduate School of Medicine, , The University of Tokyo, ; Tokyo, Japan
                [2 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Isotope Science Centre, , The University of Tokyo, ; Tokyo, Japan
                [3 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Division of Molecular Virology, Department of Microbiology and Immunology, The Institute of Medical Science, , The University of Tokyo, ; Tokyo, Japan
                [4 ]GRID grid.410804.9, ISNI 0000000123090000, Department of Ophthalmology, , Jichi Medical University Saitama Medical Centre, ; Saitama, Japan
                [5 ]GRID grid.410804.9, ISNI 0000000123090000, Department of Ophthalmology, , Jichi Medical University, ; Tochigi, Japan
                [6 ]GRID grid.417740.1, ISNI 0000 0004 0370 1830, Daiichi University of Pharmacy, ; Fukuoka, Japan
                Article
                76450
                10.1038/s41598-020-76450-2
                7656448
                33173149
                46492c42-a367-4e3c-bdb3-753626243f19
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 February 2020
                : 22 October 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008695, Japan Foundation for Applied Enzymology;
                Funded by: JSPS KAKENHI
                Award ID: JP19K09986
                Award ID: JP18K09398
                Award ID: JP18H02570
                Award Recipient :
                Categories
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                © The Author(s) 2020

                Uncategorized
                diseases,molecular medicine
                Uncategorized
                diseases, molecular medicine

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