High-throughput ribonucleic acid (RNA)-oriented technologies have become powerful tools in both basic and applied areas of biomedical research and have matured signi cantly over the past decade. The application of these tools is expected to become a means to explore, classify, and predict the biological processes underlying human diseases, justifying claims for “personalized medicine” and “targeted drug development.” Many variants of these technologies exist and new implementations continue to be developed in the hope of providing more precise relative and absolute transcript abundance measurements. Generally, they can be grouped into two categories: “hybridization” and “sequencing”-based approaches. Hybridization approaches include all forms of oligonucleotidebased microarrays [1] and complementary deoxyribonucleic acid (cDNA) microarrays [2], whereas sequencing approaches include serial analysis of gene expression (SAGE) [3], massively parallel signature sequencing (MPSS) [4], and next-generation sequencing technologies [5,6]. In the past few years, data generated by the above technologies have been overwhelming. Ef cient access to these data is expected to allow for comparison and integration of data obtained in related biological systems that will provide clinicians and researchers with an opportunity to address complex questions in an effective way.