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      Expression of Envelope Protein Encoded by Endogenous Retrovirus K102 in Rheumatoid Arthritis Neutrophils

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          Abstract

          Many patients suffering from autoimmune diseases have autoantibodies against proteins encoded by genomic retroelements, suggesting that normal epigenetic silencing is insufficient to prevent the production of the encoded proteins for which immune tolerance appears to be limited. One such protein is the transmembrane envelope (Env) protein encoded by human endogenous retrovirus K (HERV-K). We reported recently that patients with rheumatoid arthritis (RA) have IgG autoantibodies that recognize Env. Here, we use RNA sequencing of RA neutrophils to analyze HERV-K expression and find that only two loci with an intact open-reading frame for Env, HERV-K102, and K108 are expressed, but only the former is increased in RA. In contrast, other immune cells express more K108 than K102. Patient autoantibodies recognized endogenously expressed Env in breast cancer cells and in RA neutrophils but not healthy controls. A monoclonal anti-Env antibody also detected Env on the surface of RA neutrophils but very little on the surface of other immune cells. We conclude that HERV-K102 is the locus that produces Env detectable on the surface of neutrophils in RA. The low levels of HERV-K108 transcripts may contribute only marginally to cell surface Env on neutrophils or other immune cells in some patients.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              RSeQC: quality control of RNA-seq experiments.

              RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments. RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
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                Author and article information

                Contributors
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                Journal
                MICRKN
                Microorganisms
                Microorganisms
                MDPI AG
                2076-2607
                May 2023
                May 17 2023
                : 11
                : 5
                : 1310
                Article
                10.3390/microorganisms11051310
                deec6bdd-9cfc-49f9-8ad2-fcb4a30784c8
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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