Novel high-throughput deep sequencing technology has dramatically changed the way that the functional complexity of transcriptomes can be studied. Here we report on the first use of this technology to gain insight into the wide range of transcriptional responses that are associated with an infectious disease process. Using Solexa/Illumina's digital gene expression (DGE) system, a tag-based transcriptome sequencing method, we investigated mycobacterium-induced transcriptome changes in a model vertebrate species, the zebrafish. We obtained a sequencing depth of over 5 million tags per sample with strong correlation between replicates. Tag mapping indicated that healthy and infected adult zebrafish express over 70% of all genes represented in transcript databases. Comparison of the data with a previous multi-platform microarray analysis showed that both types of technologies identified regulation of similar functional groups of genes. However, the unbiased nature of DGE analysis provided insights that microarray analysis could not have achieved. In particular, we show that DGE data sets are instrumental for verification of predicted gene models and allowed us to detect mycobacterium-regulated switching between different transcript isoforms. Moreover, genomic mapping of infection-induced DGE tags revealed novel transcript forms for which any previous EST-based evidence of expression was lacking. In conclusion, our deep sequencing analysis revealed in depth the high degree of transcriptional complexity of the host response to mycobacterial infection and resulted in the discovery and validation of new gene products with induced expression in infected individuals.