Human and mouse genome and transcriptome projects have expanded the field of 'immunogenetics' beyond the traditional study of the genetics and evolution of MHC, TCR and Ig loci into the new interdisciplinary area of 'immunomics'. Immunomics is the study of the molecular functions associated with all immune-related coding and non-coding mRNA transcripts. To unravel the function, regulation and diversity of the immunome requires that we identify and correctly categorize all immune-related transcripts. The importance of intercalated genes, antisense transcripts and non-coding RNAs and their potential role in regulation of immune development and function are only just starting to be appreciated. To better understand immune function and regulation, transcriptome projects (e.g. Functional Annotation of the Mouse, FANTOM), that focus on sequencing full-length transcripts from multiple tissue sources, ideally should include specific immune cells (e.g. T cell, B cells, macrophages, dendritic cells) at various states of development, in activated and unactivated states and in different disease contexts. Progress in deciphering immune regulatory networks will require the cooperative efforts of immunologists, immunogeneticists, molecular biologists and bioinformaticians. Although primary sequence analysis remains useful for annotation of new transcripts it is less useful for identifying novel functions of known transcripts in a new context (protein interaction network or pathway). The most efficient approach to mine useful information from the vast a priori knowledge contained in biological databases and the scientific literature, is to use a combination of computational and expert-driven knowledge discovery strategies. This paper will illustrate the challenges posed in attempts to functionally infer transcriptional regulation and interaction of immune-related genes from text and sequence-based data sources.