News from the group:
CAMDA'17 Conference
22-23 July 2017,
Prague, Czech Republic
(read more)
Outstanding Presentation Prize
for PP Łabaj
at CAMDA'15 (photo)
OeAW APART fellowship
for PP Łabaj

Sequencing Quality Control (SEQC) project,
MAQC Consortium 2011–2014 (read more)
Host–parasite interactions in biocontrol, WWTF grant 2010–2013 (read more)

Power and limitations of RNA-Seq,
FDA SEQC, Nature Biotechnology (read more)
Characterization and improvement of RNA-Seq precision,
Bioinformatics (read more)
Impact of heavy tails in microarray analysis, Bioinformatics (read more)
Novel conserved repeats in sorting signals,
FEBS Journal (read more)
Sound sensation gene,
Nature communications
(read more)
RNA interference in ageing research,
Gerontology (read more)
Optimization of microarray design, probe signal interpretation
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.

Optimization of the joint penalty score J. Better probes lie closer to I0, selecting for probe uniformity, and higher in the graph, selecting for better specificity (follow link for full figure and legend) 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, Importance ranking of thermodynamic probe properties including target-side modeling (follow link for full figure and legend) the probe–target melting temperature Tm 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.


  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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