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      RNA sequencing of blood from sex- and age-matched discordant siblings supports immune and transcriptional dysregulation in autism spectrum disorder

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          Abstract

          Autism spectrum disorder (ASD) is a neurodevelopmental condition with onset in early childhood, still diagnosed only through clinical observation due to the lack of laboratory biomarkers. Early detection strategies would be especially useful in screening high-risk newborn siblings of children already diagnosed with ASD. We performed RNA sequencing on peripheral blood, comparing 27 pairs of ASD children vs their sex- and age-matched unaffected siblings. Differential gene expression profiling, performed applying an unpaired model found two immune genes, EGR1 and IGKV3D-15, significantly upregulated in ASD patients (both p adj = 0.037). Weighted gene correlation network analysis identified 18 co-expressed modules. One of these modules was downregulated among autistic individuals ( p = 0.035) and a ROC curve using its eigengene values yielded an AUC of 0.62 . Genes in this module are primarily involved in transcriptional control and its hub gene, RACK1, encodes for a signaling protein critical for neurodevelopment and innate immunity, whose expression is influenced by various hormones and known "endocrine disruptors". These results indicate that transcriptomic biomarkers can contribute to the sensitivity of an intra-familial multimarker panel for ASD and provide further evidence that neurodevelopment, innate immunity and transcriptional regulation are key to ASD 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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            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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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                antonio.persico@unimore.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 January 2023
                16 January 2023
                2023
                : 13
                : 807
                Affiliations
                [1 ]Mafalda Luce Center for Pervasive Developmental Disorders, Milan, Italy
                [2 ]GRID grid.250942.8, ISNI 0000 0004 0507 3225, Neurogenomics Division, , The Translational Genomics Research Institute, ; Phoenix, AZ USA
                [3 ]GRID grid.18887.3e, ISNI 0000000417581884, Center for Translational Genomics and Bioinformatics, , IRCCS San Raffaele Scientific Institute, ; Milan, Italy
                [4 ]Department of Genetics, Synlab Suisse SA, Bioggio, Switzerland
                [5 ]GRID grid.416651.1, ISNI 0000 0000 9120 6856, Research Coordination and Support Service, , Istituto Superiore di Sanità, ; Rome, Italy
                [6 ]GRID grid.7548.e, ISNI 0000000121697570, Child and Adolescent Neuropsychiatry Program, Department of Biomedical, Metabolic and Neural Sciences, , University of Modena and Reggio Emilia, ; Via Giuseppe Campi 287, 41125 Modena, Italy
                Article
                27378
                10.1038/s41598-023-27378-w
                9842630
                36646776
                398cf5c3-d503-4e24-a334-dc304e562ba4
                © The Author(s) 2023

                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
                : 27 August 2022
                : 2 January 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003196, Ministero della Salute;
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award ID: NET-2013-02355263
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                genetics,biomarkers,molecular medicine
                Uncategorized
                genetics, biomarkers, molecular medicine

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