While microarrays are the predominant method for gene expression profiling, understanding the variation of measurement signals is still an area of active research. Signals are probe sequence dependent, and both the interpretation of measurements and the design of microarrays need to take this into account (Kreil et al., 2006).

In view of the extremely large number of potential probe sequences to consider, many approaches to microarray design use greedy search or filtering instead of true optimization across the parameter space. Similarly, heuristic shortcuts are employed in lieu of more elaborate thermodynamic models of the probe binding process.

In Leparc et al. (2009), we have shown the improvements to specificity, sensitivity, and array uniformity that can be achieved by non-greedy set-based optimization of microarray probe design. Furthermore, we have introduced an integrated quantitative model of the effects of labelling, cross-hybridization, and other probe and target structures competing for hybridization.

In Mückstein et al. (2010), we have shown the benefits of an improved model for microarray hybridization. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the probe–target melting temperature T_m was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that also considered alternative competing conformations was twice as powerful a predictor of probe signal variation, highlighting the importance of secondary structures in the probe and target molecules.

References

1. Kreil DP, Russell RR, Russell S (2006) Microarray oligonucleotide probes. Methods Enzymol. 410, 73–98. (preprint on request | Supplement)
2. Leparc GG, Tüchler T, Striedner G, Bayer K, Sykacek P, Hofacker IL, Kreil DP (2009) Model-based probe set optimization for high-performance microarrays. Nucleic Acids Res 37, e18. (read more | Supplement)
3. Mueckstein U, Leparc GG, Posekany A, Hofacker I, Kreil DP (2010) Hybridization thermodynamics of NimbleGen microarrays. BMC Bioinformatics 11, 35. (read more)

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