Abstract
Oligonucleotide microarrays offer the potential to efficiently test for multiple organisms, an excellent feature for surveillance applications. Among these, resequencing microarrays are of particular interest, as they possess additional unique capabilities to track pathogens' genetic variations and perform detailed discrimination of closely related organisms. However, this potential can only be realized if the costs of developing the detection microarray are kept at a manageable level. Selection and verification of the probes are key factors affecting microarray design costs that can be reduced through the development and use of in silico modeling. Models created for other types of microarrays do not meet all the required criteria for this type of microarray. We describe here in silico methods for designing resequencing microarrays targeted for multiple organism detection. The model development presented here has focused on accurate base-call prediction in regions that are applicable to resequencing microarrays designed for multiple organism detection, a variation from other uses of a predictive model in which perfect prediction of all hybridization events is necessary. The model will assist in simplifying the design of resequencing microarrays and in reduction of the time and costs required for their development for new applications.