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      Qualitative and quantitative differences in endometrial inflammatory gene expression precede the development of bovine uterine disease

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          Abstract

          The transcriptome of the endometrium early postpartum was profiled to determine if inflammatory gene expression was elevated in cows which subsequently developed uterine disease. Endometrial cytobrush samples were collected at 7 days postpartum (DPP) from 112 Holstein–Friesian dairy cows, from which 27 were retrospectively chosen for RNA-seq on the basis of disease classification [ten healthy and an additional 17 diagnosed with cytological endometritis (CYTO), or purulent vaginal discharge (PVD)] at 21 DPP. 297 genes were significantly differentially expressed between cows that remained healthy versus those that subsequently developed PVD, including IL1A and IL1B (adjusted p < 0.05). In contrast, only 3 genes were significantly differentially expressed in cows which subsequently developed CYTO. Accounting for the early physiological inflammatory status present in cows which do not develop disease enhanced the detection of differentially expressed genes associated with CYTO and further expression profiling in 51 additional cows showed upregulation of multiple immune genes, including IL1A, IL1B and TNFA. Despite the expected heterogeneity associated with natural infection, enhanced activation of the inflammatory response is likely a key contributory feature of both PVD and CYTO development. Prognostic biomarkers of uterine disease would be particularly valuable for seasonal-based dairy systems where any delay to conception undermines sustainability.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              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

                Contributors
                kieran.meade@ucd.ie
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                26 October 2020
                26 October 2020
                2020
                : 10
                : 18275
                Affiliations
                [1 ]GRID grid.6435.4, ISNI 0000 0001 1512 9569, Animal and Bioscience Research Department, , Teagasc, ; Grange, Co. Meath, Ireland
                [2 ]GRID grid.8217.c, ISNI 0000 0004 1936 9705, School of Biochemistry and Immunology, , Trinity College Dublin, ; Dublin 2, Ireland
                [3 ]GRID grid.412247.6, ISNI 0000 0004 1776 0209, Department of Clinical Sciences, , Ross University School of Veterinary Medicine, ; Basseterre, St Kitts and Nevis
                [4 ]GRID grid.8217.c, ISNI 0000 0004 1936 9705, School of Medicine, , Trinity College Dublin, ; Dublin 2, Ireland
                [5 ]GRID grid.7886.1, ISNI 0000 0001 0768 2743, School of Agriculture and Food Science, , University College Dublin, ; Dublin 2, Ireland
                Article
                75104
                10.1038/s41598-020-75104-7
                7588428
                33106520
                baede49a-7491-47f0-8f11-841693f51829
                © 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
                : 15 May 2020
                : 8 October 2020
                Categories
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                © The Author(s) 2020

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                immunogenetics,transcriptomics
                Uncategorized
                immunogenetics, transcriptomics

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