Enabling technologies. Functional genomics was made possible by the significant advances that had previously been made in sequential genomics, including not only the massive efforts required to identify genome-wide DNA sequences [16] , but also the computational methods used to parse and align those sequences [17] . Sequential genomic data are deposited in large public-access databanks such as GenBank [18] , and researchers or companies who make DNA microarrays use the sequences in these databases as probes. In a similar fashion, the field of functional immunomics has now come of age as a result of advances in sequential immunomics, which consists of methods to catalogue the chemical/biological targets capable of eliciting an immune response, also known as epitopes. Computational and statistical methods are now available for automated large-scale epitope prediction (please see the next subsection), in addition to classical immunoassays such as ELISPOT [19] and tetramer staining by flow cytometry [20] that together enable high throughput identification of epitopes. Recently, a coordinated effort has been initiated by the National Institute of Allergy and Infectious Disease at the US National Institutes of Health, under the auspices of the Large-Scale Antibody and T Cell Epitope Discovery Program (in which the authors of this paper participate), to create an integrated immunome database and resources such as a toolbox of epitope prediction methods. This initiative is designed to identify immune epitopes from selected infectious agents; the information will be made freely available to scientists worldwide through the Immune Epitope Database and Analysis Resource (IEDB) [7, 21, 22] (http://www. immuneepitope.org). The sequencing information produced by this and other epitope mapping efforts being carried out will be essential for the construction of immunomic microarrays (discussed in detail below), leading to an experimental paradigm similar to that employed in functional genomics.