Leading edge analysis of transcriptomic changes during pseudorabies virus infection

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• 作者：Damarius S. Fleming ; Laura C. Miller ; ^{laura.miller@ars.usda.gov}
• 关键词：Gene expression ; Pseudorabies virus ; Swine ; Leading edge analysis ; Specifications ; Organism/cell line/tissue ; Sus scrofa domesticus/tracheobronchial lymph nodes (TBLN) ; Sex ; Male ; Sequencer or array type ; Illumina HiSeq 2000 ; Data format ; Raw Digital Gene Expression Tag Profiling sequences ; Experimental factors ; infected with feral isolate FS268 of Pseudorabies virus vs. uninfected at 1 ; 3 ; 6 ; and 14 dpi ; Experimental features ; Very brief experimental description ; Consent ; N/A ; Sample source loc
• 刊名：Genomics Data
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：10
• 期：Complete
• 页码：104-106
• 全文大小：356 K
• 卷排序：10

文摘

Eight RNA samples taken from the tracheobronchial lymph nodes (TBLN) of pigs that were either infected or non-infected with a feral isolate of porcine pseudorabies virus (PRV) were used to investigate changes in gene expression related to the pathogen. The RNA was processed into fastq files for each library prior to being analyzed using Illumina Digital Gene Expression Tag Profiling sequences (DGETP) which were used as the downstream measure of differential expression. Analyzed tags consisted of 21 base pair sequences taken from time points 1, 3, 6, and 14 days' post infection (dpi) that generated 1,927,547 unique tag sequences. Tag sequences were analyzed for differential transcript expression and gene set enrichment analysis (GSEA) to uncover transcriptomic changes related to PRV pathology progression. In conjunction with the DGETP and GSEA, the study also incorporated use of leading edge analysis to help link the TBLN transcriptome data to clinical progression of PRV at each of the sampled time points. The purpose of this manuscript is to provide useful background on applying the leading edge analysis to GSEA and expression data to help identify genes considered to be of high biological interest. The data in the form of fastq files has been uploaded to the NCBI Gene Expression Omnibus (GEO) (GSE74473) database.