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      Effects of Pseudorabies Virus Infection on the Tracheobronchial Lymph Node Transcriptome

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          Abstract

          This study represents the first swine transcriptome hive plots created from gene set enrichment analysis (GSEA) data and provides a novel insight into the global transcriptome changes occurring in tracheobronchial lymph nodes (TBLN) and spanning the swine genome. RNA isolated from draining TBLN from 5-week-old pigs, either clinically infected with a feral isolate of Pseudorabies virus or uninfected, was interrogated using Illumina Digital Gene Expression Tag Profiling. More than 100 million tag sequences were observed, representing 4,064,189 unique 21-base sequences collected from TBLN at time points 1, 3, 6, and 14 days post-inoculation (dpi). Multidimensional statistical tests were applied to determine the significant changes in tag abundance, and then the tags were annotated. Hive plots were created to visualize the differential expression within the swine transcriptome defined by the Broad Institute’s GSEA reference datasets between infected and uninfected animals, allowing us to directly compare different conditions.

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          Most cited references24

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          Systematic discovery of regulatory motifs in human promoters and 3' UTRs by comparison of several mammals.

          Comprehensive identification of all functional elements encoded in the human genome is a fundamental need in biomedical research. Here, we present a comparative analysis of the human, mouse, rat and dog genomes to create a systematic catalogue of common regulatory motifs in promoters and 3' untranslated regions (3' UTRs). The promoter analysis yields 174 candidate motifs, including most previously known transcription-factor binding sites and 105 new motifs. The 3'-UTR analysis yields 106 motifs likely to be involved in post-transcriptional regulation. Nearly one-half are associated with microRNAs (miRNAs), leading to the discovery of many new miRNA genes and their likely target genes. Our results suggest that previous estimates of the number of human miRNA genes were low, and that miRNAs regulate at least 20% of human genes. The overall results provide a systematic view of gene regulation in the human, which will be refined as additional mammalian genomes become available.
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            Next-generation tag sequencing for cancer gene expression profiling.

            We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tag-seq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.
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              Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes.

              Accumulating evidence indicates that gene order in eukaryotic genomes is not completely random and that genes with similar expression levels tend to be clustered within the same genomic neighborhoods. The mechanism behind these gene coexpression clusters is as yet unclear. In this article, plausible biochemical, genetic, evolutionary, and technological determinants of this pattern are briefly reviewed.
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                Author and article information

                Journal
                Bioinform Biol Insights
                Bioinform Biol Insights
                Bioinformatics and Biology Insights
                Bioinformatics and Biology Insights
                Libertas Academica
                1177-9322
                2015
                24 January 2016
                : 9
                : Suppl 2
                : 25-36
                Affiliations
                [1 ]Virus and Prion Research Unit, National Animal Disease Center, USDA, Agricultural Research Service, Ames, IA, USA.
                [2 ]Infectious Bacterial Diseases Research Unit, National Animal Disease Center, USDA, Agricultural Research Service, Ames, IA, USA.
                [3 ]College of Agriculture and Veterinary Medicine, University of Passo Fundo, RS, Brazil.
                Author notes
                Article
                bbi-suppl.2-2015-025
                10.4137/BBI.S30522
                4725608
                486eae6e-b4e1-4a6e-883d-0f6c0a1bfb2a
                © 2016 the author(s), publisher and licensee Libertas Academica Ltd.

                This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.

                History
                : 02 August 2015
                : 13 October 2015
                : 19 October 2015
                Categories
                Original Research

                Bioinformatics & Computational biology
                gene expression,pseudorabies virus,swine,hive plot
                Bioinformatics & Computational biology
                gene expression, pseudorabies virus, swine, hive plot

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