Analytical workflow profiling gene expression in murine macrophages

J Bioinform Comput Biol. 2015 Apr;13(2):1550010. doi: 10.1142/S0219720015500109. Epub 2015 Jan 14.

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.

Keywords: RNA-Seq; functional analysis; macrophage; transcriptome.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Computational Biology
  • Gene Expression Profiling / statistics & numerical data*
  • Gene Ontology / statistics & numerical data
  • Macrophages, Peritoneal / immunology
  • Macrophages, Peritoneal / metabolism*
  • Male
  • Mice
  • Mice, Inbred C57BL
  • Sequence Analysis, RNA / statistics & numerical data
  • Workflow