Leading edge analysis of transcriptomic changes during pseudorabies virus infection

Fleming, Damarius S.; Miller, Laura C.

doi:10.1016/j.gdata.2016.09.014

Title: Leading edge analysis of transcriptomic changes during pseudorabies virus infection

Abstract

Eight RNA samples taken from the tracheobronchial lymph nodes (TBLN) of pigs that were either infected or noninfected 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 uploadedmore »« less

Authors:: Fleming, Damarius S.; Miller, Laura C.

Publication Date:: Thu Dec 01 00:00:00 EST 2016

Research Org.:: Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; USDA

OSTI Identifier:: 1392137

Alternate Identifier(s):: OSTI ID: 1629713

Grant/Contract Number:: SC0014664; 0414701

Resource Type:: Published Article

Journal Name:: Genomics data

Additional Journal Information:: Journal Name: Genomics data Journal Volume: 10 Journal Issue: C; Journal ID: ISSN 2213-5960

Publisher:: Elsevier

Country of Publication:: Netherlands

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; 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; Sample source location

Citation Formats


                    Fleming, Damarius S., and Miller, Laura C. Leading edge analysis of transcriptomic changes during pseudorabies virus infection.  Netherlands: N. p., 2016. 
Web.  doi:10.1016/j.gdata.2016.09.014.

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                    Fleming, Damarius S., & Miller, Laura C. Leading edge analysis of transcriptomic changes during pseudorabies virus infection.  Netherlands.  https://doi.org/10.1016/j.gdata.2016.09.014

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                    Fleming, Damarius S., and Miller, Laura C. Thu .  
"Leading edge analysis of transcriptomic changes during pseudorabies virus infection".  Netherlands.  https://doi.org/10.1016/j.gdata.2016.09.014.

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@article{osti_1392137,

  title        = {Leading edge analysis of transcriptomic changes during pseudorabies virus infection},

  author       = {Fleming, Damarius S. and Miller, Laura C.},

  abstractNote = {Eight RNA samples taken from the tracheobronchial lymph nodes (TBLN) of pigs that were either infected or noninfected 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.},

  doi          = {10.1016/j.gdata.2016.09.014},

  journal      = {Genomics data},

  number       = C,

  volume       = 10,

  place        = {Netherlands},

  year         = {Thu Dec 01 00:00:00 EST 2016},

  month        = {Thu Dec 01 00:00:00 EST 2016}

}

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Works referenced in this record:

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
journal, September 2005

Subramanian, A.; Tamayo, P.; Mootha, V. K.
Proceedings of the National Academy of Sciences, Vol. 102, Issue 43, p. 15545-15550
DOI: 10.1073/pnas.0506580102

The Significance of Digital Gene Expression Profiles
journal, October 1997

Audic, Stéphane; Claverie, Jean-Michel
Genome Research, Vol. 7, Issue 10
DOI: 10.1101/gr.7.10.986

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Title: Leading edge analysis of transcriptomic changes during pseudorabies virus infection

Abstract

Citation Formats

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles journal, September 2005

The Significance of Digital Gene Expression Profiles journal, October 1997

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
journal, September 2005

The Significance of Digital Gene Expression Profiles
journal, October 1997