Model-based probe set optimization for high-performance microarrays

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Abstract
A major challenge in microarray design is the selection of highly specific oligonucleotide probes for all targeted genes of interest, while maintaining thermodynamic uniformity at the hybridization temperature. We introduce a novel microarray design framework (Thermodynamic Model-based Oligo Design Optimizer, TherMODO) that for the first time incorporates a number of advanced modelling features: (i) A model of position-dependent labelling effects that is quantitatively derived from experiment. (ii) Multi-state thermodynamic hybridization models of probe binding behaviour, including potential cross-hybridization reactions. (iii) A fast calibrated sequence-similarity-based heuristic for cross-hybridization prediction supporting large-scale designs. (iv) A novel compound score formulation for the integrated assessment of multiple probe design objectives. In contrast to a greedy search for probes meeting parameter thresholds, this approach permits an optimization at the probe set level and facilitates the selection of highly specific probe candidates while maintaining probe set uniformity. (v) Lastly, a flexible target grouping structure allows easy adaptation of the pipeline to a variety of microarray application scenarios. The algorithm and features are discussed and demonstrated on actual design runs. Source code is available on request.