DNA array technology is almost fifteen years old, and still rapidly evolving. It is one of very few platforms capable of matching the scale of sequence data produced by genome sequencing. Applications range fromanalysing single base changes, SNPs, to detecting deletion or amplification of large segments of the genome, CGH. At present, its most widespread use is in the analysis of gene expression levels. When carried out globally on all the genes of an organism, this analysis exposes its molecular anatomy with unprecedented clarity. In basic research, it reveals gene activities associated with biological processes and groups genes into networks of interconnected activities. There have been practical outcomes, too. Most notably, large-scale expression analysis has revealed genes associated with disease states, such as cancer, informed the design of new methods of diagnosis, and provided molecular targets for drug development.

At face value, the method is appealingly simple. An array is no more than a set of DNA reagents for measuring the amount of sequence counterparts among them RNAs of a sample. However, the quality of the result is affected by several factors, including the quality of the array and the sample, the uniformity of hybridisation process, and the method of reading signals. Errors, inevitable at each stage, must be taken into account in the design of the experiment and in the interpretation of results. It is here that the scientist needs the help of advanced statistical tools.

Dr. Stekel is a mathematician with several years of experience in the microarray field. He has used his expertise in a company setting, developing advanced methods for probe design and for the analysis of large, complex data sets.