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Hybridization thermodynamics of NimbleGen Microarrays
VerfasserMueckstein, Ulrike ; Leparc, Germán G. ; Posekany, Alexandra ; Hofacker, Ivo ; Kreil, David P.
Erschienen in
BMC Bioinformatics, 2010, Jg. 11,
ErschienenBioMed Central (BMC), 2010
DokumenttypAufsatz in einer Zeitschrift
URNurn:nbn:at:at-ubbw:3-674 Persistent Identifier (URN)
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Hybridization thermodynamics of NimbleGen Microarrays [0.4 mb]
Zusammenfassung (Englisch)


While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets.


We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature T m was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex.


This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals.