Infectious diseases cost lives of over 14 million people each year (WHO, 2003). Similarly, diseases reduce crop production by more than 10% worldwide ( ISPP, 1998). Therefore, it is of primary importance to effectively control infectious diseases both from medical and agricultural points of view. The study of host–pathogen interactions is instrumental for such purposes. Host eukaryotes are constantly exposed to attacks by microbes seeking to colonize and propagate in host cells. To counteract them, host cells utilize a whole battery of defence systems to combat microbes. However, in turn, successful microbes evolved sophisticated systems to evade host defence. As such, interactions between hosts and pathogens are perceived as evolutionary arms races between genes of the respective organisms (Bergelson et al., 2001; Kahn et al., 2002; Woolhouse et al., 2002). Any interaction between a host and its pathogen involves alterations in cell signalling cascades in both partners, that may be mediated by transcriptional or post-translational changes. Here, the major challenge for researchers is how to select target genes to be studied in detail from among thousands of genes encoded in the genome. Transcriptomics is one of the methodologies to serve this purpose. Analytical techniques for transcriptomics include differential display (DD; Liang and Pardee, 1992), cDNA-AFLP (Bachem et al., 1996), random EST sequencing (Kamoun et al., 1999), microarray (Schena et al., 1995), serial analysis of gene expression (SAGE; Velculescu et al., 1995) and massively parallel signature sequencing (MPSS; Brenner et al., 2000). Among them, microarray is recently used more frequently than other platforms. Several excellent reviews are available for the use of micoarray for studying host–pathogen interactions (Cummings and Relman, 2000; Diehn and Relman, 2001; Kato-Maeda et al., 2001; Wan et al., 2002; Bryant et al., 2004). In this context it is quite remarkable that most of the gene expression studies addressing host–pathogen interactions in reality examined either host or pathogen separately. However, the simultaneous monitoring of gene expression of both host and pathogen (‘interaction transcriptome’), preferably during the infection process and in situ, especially in the field of plant–microbe interactions, is at stakes and has already been advocated by Birch and Kamoun (2000). Also in our mind, this approach is necessary to elucidate the host–pathogen interplay in molecular detail, although presently available techniques cannot discriminate between the transcriptomes of both organisms. In this article, we present a novel method called SuperSAGE, which has proven potential for an analysis of the interaction transcriptome.