The comprehensive and simultaneous analysis of all genes in a biological sample is a powerful capability attributed to RNA-Seq technology. The ability to analyze the entire transcriptome with RNA-Seq demands analysis that effectively addresses the summary action of genes at the categorical level. Maintaining biological relevance is also important, both for network-level effects and the individual genes that may causally influence these larger changes. In this work, transcriptome analysis of two conditions utilizes a pair-wise comparison between control and immunologically challenged mice. Individual genes were evaluated for successful fit of the model (using a Negative Binomial distribution), then tested for differential expression (FDR-adjusted p-value < 0.05) and grouped into functional categories. A total of 2,079 differentially expressed transcripts representing 1,884 genes were detected. Clustering of enriched Gene Ontology terms Biological Processes, Molecular Functions, and KEGG pathways categories uncovered functional clusters relevant to the immunological response expected from the samples studied (defense and inflammatory response, Enrichment Score = 11.2; leukocyte migration, Enrichment Score = 3.1). These results provide a context to the gene expression differences. Consistent with previous microarray-level transcriptomic studies, our work illustrates the broad analysis and fine detail available with current high throughput RNA sequencing.