The comparison of several statistical methods currently used for detection of differentially expressed genes was attempted both by a simulation approach and by the analysis of data sets of human expressed sequence tags, obtained from UniGene. In the simulated mixed case, mimicking a situation close to reality, the general chi(2) test was unexpectedly the most efficient in multiple tag sampling experiments, especially when dealing with variations affecting weakly expressed genes. On the other hand, Audic and Claverie's method proved the most efficient for detecting differences in gene expression when dealing with pairwise comparisons. By applying the above methods on UniGene-based data sets concerning two human kidney tumours compared with normal kidney tissue, three novel genes overexpressed in these tumours were identified. Software and additional information on statistical methodologies, simulation approach and data are available at http://telethon.bio.unipd.it/bioinfo/IDEG6/.