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Nucleic Acids Research Advance Access originally published online on December 22, 2008
Nucleic Acids Research 2009 37(3):e18; doi:10.1093/nar/gkn1001
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Nucleic Acids Research, 2009, Vol. 37, No. 3 e18
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods Online

Model-based probe set optimization for high-performance microarrays

Germán Gastón Leparc1,*, Thomas Tüchler1, Gerald Striedner2, Karl Bayer2, Peter Sykacek1, Ivo L. Hofacker3 and David P. Kreil1,*

1WWTF Chair of Bioinformatics, 2Institute of Applied Microbiology, Boku University Vienna, Muthgasse 18, 1190 Vienna and 3Theoretical Biochemistry Group, Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria

*To whom correspondence should be addressed. Tel: +43 1 36006 6830/6202/6845; Fax: +43 1 36006 6847; Email: thermodo08{at}boku.ac.at

Received July 22, 2008. Revised October 30, 2008. Accepted November 28, 2008.

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.


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