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      Identification of Unique microRNA Profiles in Different Types of Idiopathic Inflammatory Myopathy

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          Abstract

          Dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotizing myopathy (IMNM), and inclusion body myositis (IBM) are four major types of idiopathic inflammatory myopathy (IIM). Muscle biopsies from each type of IIM have unique transcriptomic profiles. MicroRNAs (miRNAs) target messenger RNAs (mRNAs), thereby regulating their expression and modulating transcriptomic profiles. In this study, 18 DM, 12 IMNM, 6 AS, 6 IBM, and 6 histologically normal muscle biopsies underwent miRNA profiling using the NanoString nCounter system. Eleven miRNAs were exclusively differentially expressed in DM compared to controls, seven miRNAs were only differentially expressed in AS, and nine miRNAs were specifically upregulated in IBM. No differentially expressed miRNAs were identified in IMNM. We also analyzed miRNA-mRNA associations to identify putative targets of differentially expressed miRNAs. In DM and AS, these were predominantly related to inflammation and cell cycle progression. Moreover, our analysis showed an association between miR-30a-3p, miR-30e-3p, and miR-199b-5p downregulation in DM and the upregulation of target genes induced by type I interferon. In conclusion, we show that muscle biopsies from DM, AS, and IBM patients have unique miRNA signatures and that these miRNAs might play a role in regulating the expression of genes known to be involved in IIM pathogenesis.

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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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            fastp: an ultra-fast all-in-one FASTQ preprocessor

            Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

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                Journal
                CELLC6
                Cells
                Cells
                MDPI AG
                2073-4409
                September 2023
                September 02 2023
                : 12
                : 17
                : 2198
                Article
                10.3390/cells12172198
                46de189b-3b6d-4c84-9ecb-a986a9fea23c
                © 2023

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

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