Analytical workflow profiling gene expression in murine macrophages

Scott E. Nixon, Dianelys Gonzalez-Peña, Marcus A. Lawson, Robert H. McCusker, Alvaro G. Hernandez, Jason C. O'Connor, Robert Dantzer, Keith W. Kelley, Sandra L. Rodriguez-Zas

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Comprehensive and simultaneous analysis of all genes in a biological sample is a capability of RNA-Seq technology. Analysis of the entire transcriptome benefits from summarization of genes at the functional level. As a cellular response of interest not previously explored with RNA-Seq, peritoneal macrophages from mice under two conditions (control and immunologically challenged) were analyzed for gene expression differences. Quantification of individual transcripts modeled RNA-Seq read distribution and uncertainty (using a Beta Negative Binomial distribution), then tested for differential transcript expression (False Discovery Rate-adjusted p-value < 0:05). Enrichment of functional categories utilized the list of differentially expressed genes. A total of 2079 differentially expressed transcripts representing 1884 genes were detected. Enrichment of 92 categories from Gene Ontology Biological Processes and Molecular Functions, and KEGG pathways were grouped into 6 clusters. Clusters included defense and inflammatory response (Enrichment Score = 11:24) and ribosomal activity (Enrichment Score = 17:89). Our work provides a context to the fine detail of individual gene expression differences in murine peritoneal macrophages during immunological challenge with high throughput RNA-Seq.

Original languageEnglish (US)
Article number1550010
JournalJournal of bioinformatics and computational biology
Volume13
Issue number2
DOIs
StatePublished - Apr 25 2015

Keywords

  • RNA-Seq
  • functional analysis
  • macrophage
  • transcriptome

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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